AI Agent Engineering: Building Agentic Systems for Enduring Value

Introduction

The emergence of agentic AI systems represents an exciting evolution in artificial intelligence—from isolated models performing narrowly defined tasks to sophisticated agents capable of autonomous decision-making and complex problem-solving. This shift unlocks unprecedented opportunities to develop AI solutions that not only address immediate needs but also create compounding value over time. However, many current implementations, while impressive, risk becoming fleeting novelties rather than enduring assets, given the rapid pace at which underlying technologies evolve.

How can we strategically approach AI Agent Engineering—what I refer to as Agenteering—to build systems that deliver sustained value? While model-specific optimizations may yield immediate performance gains, significant untapped potential lies in designing cohesive architectures that balance immediate utility with long-term adaptability.

Building upon the Action-Brain-Context (ABC) framework, this article offers a structured approach to AI agent engineering that explores how different components contribute to value creation across varying time horizons. By examining each element of the ABC framework—Action, Brain, and Context—we'll investigate how thoughtful engineering decisions enhance agentic AI systems today while establishing robust foundations that can evolve with advancing technologies tomorrow.

We begin by clarifying the essence of what constitutes a true AI agent, emphasizing agency as the crucial differentiator between deterministic workflows and systems capable of autonomous decision-making. We then delve into how the ABC framework scales from individual agents to advanced multi-agent systems, identifying orchestration patterns that unlock emergent capabilities far beyond what a single agent could achieve alone. The article explores how different aspects of these systems may create more enduring value than others, before examining best practices for system architecture, world integration, and process execution in the development of effective agentic AI systems.

Whether you're reinventing customer experiences, redefining operational paradigms, revolutionizing healthcare delivery, disrupting financial services, transforming education, or pioneering innovation in any other industry, and whether you're an independent practitioner, part of a small team, or operating within a large enterprise, the AI agent engineering principles outlined here will equip you to build systems that maintain their relevance and utility—even as the technologies they rely on continue to evolve.

The Evolution of AI

From AI Models to Autonomous Agents:

The journey from basic AI models to truly autonomous agents represents a profound shift in artificial intelligence. Rather than viewing this as a series of technical definitions, let's explore this evolution as a progression of increasing capability and autonomy.

NVIDIA’s Jensen Huang has framed the main phases of AI evolution as follows:

Perception AI: Perception AI represents the first major wave—systems trained to recognize patterns and extract meaning from data. These include speech recognition, computer vision, medical imaging analysis, and other technologies that transform raw sensory data into structured information. While powerful, these systems primarily interpret rather than create or act.

Generative AI: Generative AI marks the next significant advance—models that can produce novel content based on patterns learned from training data. These systems generate text, images, code, and other media that increasingly resemble human-created work. While impressive in their creative capabilities, most generative applications still operate as tools directly controlled by humans rather than perform tasks autonomously.

Agentic AI: Moving beyond generation to action, we encounter AI agents—systems that harness foundation models’ reasoning capabilities and connect them to external tools, enabling autonomous goal pursuit through observation, planning, and action. At the frontier of current AI development, we find Agentic AI Systems—sophisticated networks of specialized agents working in concert to tackle complex challenges.

Physical AI: Looking further ahead, Physical AI represents the embodiment of agentic capabilities in the physical world, for example through autonomous vehicles and general robotics.

The progression from perception to generation to agency and embodiment isn’t merely a technical evolution—it represents a fundamental shift in how we create value with AI.

Key Concepts in Agentic AI

Before exploring how to engineer these systems, let’s first define the key concepts in Agentic AI that will guide our discussion:

AI Agents: Systems that autonomously pursue goals through a continuous cycle of perception, reasoning, and action. Unlike static AI applications, agents maintain persistent context, strategically select and use tools, and adapt their approaches based on environmental feedback without requiring step-by-step human guidance.

Agentic AI Systems: Integrated networks of multiple AI agents working together as a cohesive system to accomplish broader objectives than any single agent could achieve alone. These systems manage the coordination, communication, and resource allocation between specialized agents, enabling them to collectively tackle complex tasks across multiple domains while maintaining alignment with overarching goals.

AI Agent Engineering (Agenteering): The discipline of designing, building, and optimizing AI systems across the autonomy spectrum—from individual agents to orchestrated multi-agent networks. It encompasses the strategic selection of foundation models, the integration of contextual knowledge and tools, the design of feedback mechanisms, and the implementation of coordination protocols that enable these systems to deliver compounding value through continuous adaptation and learning.

These definitions highlight a crucial insight: true AI agents are defined by their agency—their capacity to make autonomous decisions and take independent actions in pursuit of goals. This distinguishes them from deterministic workflows or simple chains of LLM calls that lack adaptive decision-making capabilities.

This distinction is critical for anyone investing in agentic AI, as systems with genuine agency can adapt to novel situations, continuously improve through experience, and operate effectively in ambiguous or changing environments—creating substantially more value in complex domains.

Therefore, when evaluating a system, we should ask: Does it possess agency? Can it make meaningful decisions and adapt based on feedback? Or is it merely executing predetermined steps?

Building systems that move beyond basic automation toward true agency requires thoughtful architecture, orchestration, security, and alignment decisions—shifting from building tools that execute commands to designing partners that collaborate toward shared objectives. Organizations seeing the greatest returns aren't necessarily those with the most advanced models, but those engineering systems that grow more valuable over time through accumulated context, refined workflows, and deepening alignment with human needs.

In the following section, we'll explore how the Action-Brain-Context (ABC) framework provides a blueprint for making these engineering decisions strategically, helping you build agents that deliver lasting value rather than fleeting novelty.

The ABC Framework: A Blueprint for Building Agentic AI Systems

The Action-Brain-Context (ABC) framework, first proposed in 2023, continues to serve as a simple yet powerful blueprint for understanding and engineering effective AI agents. As we move further into the agentic era, this framework reveals even deeper insights when viewed through multiple levels of abstraction — from individual AI agents to complex agentic AI systems.

Core Components: The Foundation of Every AI Agent

At its essence, the framework consists of three interdependent pillars working in harmony to create effective AI agents:

Brain: The Engine of Autonomous Reasoning

At the core of every effective AI agent lies its “Brain” — the reasoning engine that processes information and makes decisions. This cognitive center, powered by large language models (LLMs), does far more than generate text; it orchestrates the agent’s entire thinking process. When an AI agent encounters a complex problem, the Brain breaks it down into manageable components, applies appropriate reasoning patterns, and maintains coherence across multiple steps.

The Brain’s effectiveness isn’t merely a function of model size or parameter count. Rather, it emerges from how we structure the model’s approach to problems — through thoughtful prompting strategies, deliberate reasoning frameworks, and careful orchestration of its cognitive flow. A well-engineered Brain doesn’t just respond to queries; it actively thinks through challenges, weighs alternatives, and arrives at conclusions that drive meaningful action.

Context: The Foundation of Knowledge and Memory

While the Brain provides reasoning power, the “Context” pillar grounds that reasoning in relevant knowledge and experience. Context transforms an abstract reasoning engine into a domain-aware AI agent capable of making informed decisions. This pillar encompasses everything from specialized knowledge bases to conversation history, from user preferences to situational awareness.

Effective context management creates a rich informational environment in which reasoning can operate. When an AI agent accesses domain-specific knowledge, recalls previous interactions, or understands the current situation, it moves beyond generic responses toward truly personalized and relevant assistance. The Context pillar enables AI agents to build relationships over time — remembering past interactions, learning from experience, and adapting to specific domains with increasing sophistication.

Action: The Bridge to Real-World Impact

The most brilliant reasoning remains theoretical without the ability to act. The “Action” pillar bridges this gap, connecting cognitive processes to tangible outcomes in the world. Through tool usage, API integrations, and workflow orchestration, AI agents translate decisions into results that create real value.

When an AI agent invokes a search API to find information, queries a database to retrieve records, or triggers an automation to complete a process, it extends its influence beyond conversation into practical utility. The Action pillar determines whether an agent remains confined to the digital realm of text generation or becomes a productive partner capable of accomplishing meaningful real-world task.

Beyond individual tools, the Action component also encompasses entire workflow paradigms—from traditional human-centric processes to emerging AI-native approaches that reimagine execution around AI's unique capabilities. These workflow choices significantly influence how effectively agents can leverage their tools and reasoning abilities in practice.

While tools and their execution fall primarily within the Action pillar, the protocols and standards that enable tool discovery and integration often bridge both Action and Context components. These integration standards help agents understand what capabilities are available (Context) and how to effectively utilize them (Action) — a connection we'll explore further when discussing implementation best practices.

The ABC in Motion: Continuous Improvement

The ABC framework operates as a dynamic system where the three components work together to enable ongoing enhancement:

• The Brain applies its reasoning capabilities to increasingly rich information. While model retraining could improves performance, agents can also become more effective through better utilization of accumulated context and experience, even with the same underlying model.
• The Context serves as the agent's evolving memory and knowledge base. As a personal assistant agent handles more tasks, it builds comprehensive understanding of user preferences, past interactions, and effective approaches—creating a foundation that enhances future operations. OpenAI's recent updates to ChatGPT memory exemplifies this perfectly.
• The Action component develops more sophisticated execution patterns over time. Through repeated interactions, the agent refines its tool selection, better understands API behaviors, and develops more efficient action sequences based on operational feedback.

Tying these components together are systematic evaluation processes that assess each interaction, capturing what works and what doesn't across all three dimensions. By designing systems that preserve these evaluative insights, practitioners create agents whose performance compounds through experience rather than depending solely on underlying technological upgrades.

Scaling ABC: From Individual AI Agents to Agentic AI Systems

The true power of the ABC framework is unleashed when we recognize how it scales across different levels of abstraction. What seems like a single AI agent at one level may, in fact, be a sophisticated ecosystem of specialized AI agents at another—each with its own ABC components working in harmony.

The Emergence of Multi-Agent AI Systems

As practitioners tackle increasingly complex challenges, they often evolve from single AI agents toward interconnected systems of specialized AI agents. Rather than building a monolithic “super agent” responsible for every aspect of a workflow, they deploy multiple AI agents with focused expertise—each optimized for specific functions within a broader process.

This specialization creates several advantages. Each AI agent can be engineered with the specific models, context, and tools required for its domain, avoiding the compromises inherent in one-size-fits-all approaches. Components can be updated or replaced independently, dramatically reducing the complexity of managing any single AI agent. When failures or limitations arise in one AI agent, they do not have to compromise the entire workflow, improving overall system resilience. Most importantly, these systems can grow organically, with new AI agents adding specialized capabilities as needs evolve, rather than requiring complete redesigns.

These multi-agent AI systems aren’t a departure from the ABC framework—they’re an extension of it, applying the same principles at a higher level of organization. The framework’s consistency across different scales of implementation provides a common language for discussing and designing increasingly sophisticated systems.

Blurring Boundaries: The Fractal Nature of Agency

As these multi-agent AI systems become more sophisticated, we encounter an interesting phenomenon: the traditional distinctions between “agents,” “tools,” and “services” begin to blur. In simpler implementations, these categories seem clear—an AI agent uses tools (like calculators or search engines) and calls services (like databases or APIs). But in advanced systems, these boundaries become increasingly fluid.

Consider an example of nested agency: A primary AI agent might invoke what appears to be a “code generation tool” to create a script. Yet that “tool” could itself be another AI agent that breaks down the coding task, reasons about implementation details, and tests its output before returning the result. From the primary agent’s perspective, it simply called a tool and received a result. But internally, that tool employed sophisticated agency to accomplish its task, making complex decisions and potentially using other tools in turn.

This nested structure creates a fractal-like quality in advanced agentic AI systems, where agency exists at multiple levels of abstraction. The same ABC framework applies at each level, but the interpretation of what constitutes “Brain,” “Context,” and “Action” shifts depending on your perspective. What appears as a simple Action from one vantage point might contain an entire ABC cycle from another.

ABC Units as Building Blocks

This fractal quality of agency suggests a powerful architectural approach: we can conceptualize components of agentic AI systems as “ABC units”—discrete modules with their own Brain, Context, and Action capabilities that can be composed into more complex arrangements. Rather than seeing the ABC framework as applying only to end-to-end agents, we can use it to design modular components that combine in various patterns:

• Sequential chains: Where the output of one AI agent becomes input for the next, creating assembly-line-like workflows. A research agent might gather information, pass its findings to an analysis agent, which then feeds insights to a report-generation agent.
• Hierarchical structures: Where a coordinator AI agent delegates subtasks to specialized AI agents and synthesizes their results. This mirrors organizational structures where managers assign work to team members with different expertise.
• Collaborative networks: Where AI agents interact as peers, sharing information and collaborating on complex tasks without rigid hierarchies. This approach excels in creative domains where multiple perspectives need to blend seamlessly.
• Competitive ensembles: Where multiple AI agents tackle the same problem using different approaches, with results evaluated and selected based on quality metrics. This creates redundancy and often improves output quality through diversity of thought.

The flexibility of these arrangements allows builders to design agentic AI ecosystems that match their specific workflows, organizational structures, and problem domains—all while maintaining the conceptual clarity of the ABC framework.

The System as a Meta AI Agent

Moving from these building-block arrangements to the highest level of abstraction, we can take a step back and view an entire multi-agent AI system through the same ABC lens. This perspective shifts our focus from individual components to the system as a unified whole—a meta-agent with emergent capabilities beyond those of its constituent parts.

The Brain of this meta-agent comprises the collective reasoning capabilities of all component AI agents, along with the orchestration logic that coordinates their activities. Its Context includes not just the knowledge available to individual AI agents, but the shared information spaces and communication channels that enable collaboration. The Action component encompasses the combined capabilities of all tools and integrations across the system, coordinated to achieve higher-level objectives. Crucially, the continuous-improvement loop operates both within individual AI agents and across the system as a whole, creating multiple feedback loops that drive continuous improvement at different scales.

This perspective helps practitioners design systems that function cohesively rather than as collections of disconnected agents, ensuring alignment toward common goals while maintaining the specialized focus of individual components.

Applying the ABC Framework: From Concept to Practice

The ABC framework’s ability to scale across different levels of abstraction—from individual AI agents to complex agentic systems—makes it a powerful tool for navigating AI agent engineering. Rather than providing just a conceptual model, it offers a structured approach to making practical decisions about how to build, deploy, and evolve agentic AI solutions.

By viewing your AI initiatives through this lens, you gain clarity on critical questions: Where should intelligence reside in your system? How should components communicate and share context? Which actions should be automated versus requiring human oversight? These decisions shape not just immediate functionality but long-term adaptability and value creation.

In the following sections, we’ll explore how each element of the ABC framework contributes to value creation across different time horizons—from quick wins to strategic advantages—and examine best practices for implementation across various domains.

Value Horizons: Prioritizing AI Agent Engineering for Enduring Impact

When building agentic AI systems, practitioners must strategically allocate limited resources across different time horizons. By understanding which engineering investments create enduring value versus those requiring frequent updates, organizations can balance immediate gains with long-term competitive advantage. Let's examine how each component of the ABC framework contributes to value creation across these different time horizons.

Brain: Strategic Model Utilization for Lasting Value

Model selection and optimization for specific model versions deliver immediate performance gains but require frequent updates as models evolve. Prompt engineering techniques that work brilliantly with one model may need substantial revision for another. A great example is Chain-of-Thought prompting, which proved extremely useful across many LLMs. However, when reasoning models like OpenAI's o1 emerged with reasoning steps baked in, they required different prompting strategies to fully leverage their capabilities, necessitating new engineering efforts even while the original techniques remained valuable for the non-reasoning models. This model-specific knowledge, while valuable for immediate optimization, tends to depreciate rapidly with each new model release.

Fine-tuning current models can provide significant value in specific scenarios, particularly for specialized domains with unique terminology or for optimizing response formats and styles. In regulated industries like healthcare or finance, fine-tuning can help ensure compliance with domain-specific requirements. For instance, a healthcare AI might be fine-tuned to consistently follow HIPAA-compliant language patterns or to accurately use medical terminology in a way that reduces liability risks. Similarly, financial services organizations might fine-tune models to adhere to specific disclosure requirements when discussing investment products.

However, these benefits must be weighed against the maintenance challenges as models evolve. Fine-tuned models often require regular updates to maintain their advantage over increasingly capable base models. As foundation models improve, capabilities that once required fine-tuning—like consistent formatting or specialized knowledge—are increasingly available out-of-the-box. This evolution can diminish the relative advantage of fine-tuning over time, especially for more general use cases. For model knowledge ingestion, fine-tuning is generally not the first choice because it requires continuous training updates to keep pace with new information—a process typically more cumbersome than well-implemented Retrieval Augmented Generation (RAG) methods, which can access updated information without model retraining. While fine-tuning offers more durability than prompt engineering tied to specific model versions, it still represents a short-to-medium term investment that requires ongoing attention.

Most practitioners are using foundation models developed by specialized AI labs rather than developing those models themselves. In that sense, there is naturally limited long-term value in customizing specific model versions, as newer releases might quickly render these customizations obsolete. However, by stepping back a level to focus on broader model categories rather than individual models, we find more durable approaches. Understanding how reasoning-focused models differ from standard generative models creates knowledge that remains valuable across model generations. Building systems that can leverage both types of models offers flexibility that outlasts any single model. This category-level understanding represents medium-term value that persists through multiple model iterations, helping organizations adapt strategically as the landscape evolves.

The most enduring value comes from multi-agent cognitive architectures that orchestrate specialized models and capabilities. These systems transcend the limitations of any single model by creating emergent intelligence through thoughtful coordination. A research agent might leverage a model optimized for search and retrieval, while an analysis agent uses a reasoning-focused model, and a writing agent employs one with strong language generation. These multi-agent architectures adapt naturally to model evolution—individual agents can be upgraded or replaced without disrupting the overall system. This modularity creates significant durability, even as it introduces complexity challenges.

The most sophisticated practitioners approach this balance between flexibility and complexity strategically—building systems with clear boundaries, robust monitoring, and graceful degradation mechanisms. Their orchestration patterns—how tasks are decomposed, how agents collaborate, and how errors are contained—become institutional knowledge that creates lasting competitive advantage as foundation models continue to advance.

Context: Building Knowledge Assets That Appreciate Over Time

While the Brain component focuses on reasoning capabilities, the Context component addresses how knowledge is made available to AI agents. At its core, effective context management serves one fundamental purpose: retrieving the most relevant information when needed—a function that remains essential regardless of technological evolution.

Providing accurate and relevant context creates substantial immediate value. Even the most sophisticated models are subject to the "garbage in, garbage out" principle, making effective retrieval mission-critical for any AI system. The specific implementation may evolve rapidly—from Cache Augmented Generation (CAG) leveraging massive context windows to traditional Retrieval Augmented Generation (RAG) with continually improving document processing, chunking strategies, and embedding technologies—but these advances all represent better ways to accomplish the same essential function: delivering the right information at the right time.

When categorizing context engineering efforts by time horizon, short-term value comes from implementing the best available retrieval technologies, which deliver immediate impact but require regular updates as capabilities evolve. A company might invest in today's state-of-the-art vector database or customized embedding model, gaining immediate performance improvements that nonetheless require upgrades as new approaches emerge.

Medium-term value emerges from knowledge organization that improves retrieval quality regardless of specific technology. Well-structured knowledge bases and domain-specific ontologies enhance precision across multiple generations of retrieval technology. For instance, a financial services firm's comprehensive taxonomy of investment products and regulatory requirements remains valuable even as retrieval technologies evolve from keyword search to embeddings to other approaches. However, if maintaining these structures requires significant manual effort, they face scalability challenges as information volumes grows.

The most enduring investments create self-improving knowledge systems with robust feedback loops. These systems implement "organizational memory" that captures both explicit and tacit knowledge, continuously learning which information proves most useful in different scenarios. Unlike medium-term investments in static (though well-organized) knowledge structures, these systems become increasingly valuable through compounding learning effects. A healthcare organization's system might track which resources physicians find most helpful for specific conditions, automatically elevating those resources in future similar cases while identifying knowledge gaps to be filled. This approach creates compounding returns while reducing dependency on human-intensive processes that don't scale.

Practitioners should balance immediate needs with approaches that create lasting value—implementing today's best retrieval technologies while building knowledge structures and feedback systems that retains value regardless of technological evolution.

Action: Building Tool Ecosystems That Evolve With Technology

While the Brain provides reasoning and the Context supplies knowledge, the Action component gives AI agents the ability to affect the world through tools, APIs, and integrations. This capability transforms agents from passive information processors into systems that can accomplish real work. However, like other components, action capabilities vary significantly in their durability.

The most immediate value comes from connecting agents to existing tools and APIs. Integrations with search engines, databases, CRM systems, or productivity applications allow agents to access information and perform tasks beyond their built-in capabilities. These initial integrations often focus on high-value, frequently used tools that deliver immediate productivity gains. However, many early tool integrations are tightly coupled to specific model behaviors or response formats—parsers that expect particular output structures, error handling based on known model limitations, or prompts tailored to a single model. Such integrations frequently require updates as models evolve.

More durable value emerges when focusing on standardizing tool interfaces rather than optimizing for specific models. The development of consistent API patterns, robust parsing systems that handle variable inputs, and clear feedback mechanisms creates a more sustainable foundation for tool integration. These standardized interfaces maintain their utility across model generations because they emphasize the contract between agents and tools rather than the particularities of any given model.

The highest level of durability comes from reimagining workflow paradigms themselves. Traditional human-centric workflows—designed around human cognitive limitations and organizational structures—often constrain AI capabilities. By contrast, AI-native workflows that build processes around AI's unique strengths create longer-lasting value. These approaches question fundamental assumptions about how work should be structured, eliminating unnecessary intermediary steps and leveraging AI's ability to handle complexity in ways humans cannot. As AI models continue to evolve, these reimagined workflows appreciate in value, providing a strategic advantage that transcends specific tool implementations or model generations.

Value Patterns: Cross-Component Strategies for Enduring Impact

Looking across the time horizons for Brain, Context, and Action components reveals consistent patterns in how value creation evolves in agentic AI systems.

At the short-term horizon, value flows from tactical optimizations tied to specific technologies: crafting prompts for particular models, deploying cutting-edge retrieval methods, and building custom tool integrations. These approaches deliver immediate results but require vigilant updating as the technological landscape shifts. They represent necessary capabilities for today's challenges, even as their value naturally depreciates over time.

Medium-term value takes shape through approaches that rise above specific implementations while adapting to technological change. By understanding broader model categories rather than specific versions, creating well-structured knowledge with consistent organization, and standardizing tool interfaces, organizations build assets that retain their utility across technological generations. These investments create stability amid constant innovation.

The most enduring value blossoms from systems designed for autonomous evolution. Whether through orchestration architectures that seamlessly incorporate new models, knowledge systems that continuously refine themselves through feedback, or workflows reimagined around AI's unique capabilities—these approaches share a powerful characteristic: they transcend human limitations through intelligent adaptation. Rather than requiring constant maintenance, they improve through operation.

This progression highlights a fundamental shift in AI engineering: from building static tools to nurturing dynamic ecosystems. The most valuable investments don't just solve today's problems with today's technology; they establish living frameworks that naturally incorporate tomorrow's advances while preserving accumulated wisdom.

Organizations that recognize these patterns can weave strategies where advances in one area naturally enhance capabilities in others, creating compounding returns that accelerate over time.

Best Practices: Engineering Effective Agentic AI Systems

As we move from the conceptual ABC framework to practical implementation, we can identify three critical dimensions for creating effective agentic AI systems:

• System Architecture: Creating intelligent agent orchestration that brings the Brain component to life through thoughtful design of how agents communicate, coordinate, and collaborate
• World Integration: Connecting AI with the external world by expanding Context capabilities through standardized interfaces between agents and their environment
• Process Execution: Transforming how work gets done by realizing the Action potential through workflows that leverage AI's unique capabilities

These dimensions naturally map to the ABC framework - System Architecture brings the Brain component to life, World Integration underpins the Context capabilities, Process Execution realizes the Action potential.

All of them are unified by a cross-cutting dimension: Human-AI Synergy—which shapes the evolution of intelligent systems over time. From initial development where human guidance plays a central role, toward increasingly sophisticated systems where the balance shifts. Even as systems become more autonomous in their improvement, the human element remains essential in providing direction, purpose, and values.

Let's dive into them one by one.

System Architecture: Agent Communication and Orchestration

The architecture of agentic systems determines how different components communicate, coordinate, and collaborate to solve complex problems. This foundation shapes everything from performance and cost efficiency to adaptability and maintainability.

Composition Patterns: From Simple Chains to Complex Networks

In our earlier illustration of the ABC framework, we identified how agent components can be composed into various architectural patterns. We specifically discussed sequential chains (where outputs from one agent become inputs for another), hierarchical structures (where coordinator agents delegate to specialized agents), collaborative networks (where agents interact as peers), and competitive ensembles (where multiple approaches tackle the same problem). These patterns emerged naturally from our analysis of how ABC units can be connected to solve complex problems. Not surprisingly, they are also aligned with the patterns Anthropic presented in their research on building effective agents, which is derived from their practical implementation experience. Anthropic's concept of "Augmented LLMs" closely parallels our ABC unit, where tools and capabilities extend a model's core reasoning abilities.

These patterns also facilitate strategic model allocation across the system. By understanding each pattern's strengths, architects can deploy different model types where they create maximum value:

• Advanced reasoning models excel as orchestrators in hierarchical structures, handling complex planning and decision-making
• Specialized models with domain-specific training perform best in collaborative networks where deep expertise matters
• Lightweight, efficient models work well in sequential chains for routine processing steps

This strategic model deployment creates "brain-and-body" systems. For example, an orchestrator agent powered by a reasoning model might coordinate a team of specialized agents running on smaller, more efficient models—maximizing performance while controlling costs.

The meta-prompting technique extends this approach by using reasoning models to generate and refine instructions for other models in the system. This creates a continuous improvement cycle where the system's architecture can evolve through data-driven prompt optimization rather than manual adjustments.

While these patterns provide a powerful vocabulary for agent orchestration design, implementations should follow a progressive approach. Start with the simplest solution that addresses the core need, then add complexity only when it demonstrably improves outcomes.

For many applications, well-crafted prompts with retrieval and in-context examples provides a good foundation. When more structure is needed, deterministic workflows provide predictability for well-defined tasks. Only when flexibility and model-driven decision-making are truly needed should teams expand into fully agentic systems.

Agent Framework Ecosystem: Platforms for Implementing Agent Architectures

The evolution of popular agent frameworks validates our patterns for agent interactions and provides platforms to implement them:

• Langchain is one of the earliest and most influential frameworks in this space. It began by implementing simple sequential chains—where the output of one component becomes the input for the next. This approach directly maps to the sequential chaining pattern we discussed. As the field evolved, so did Langchain, expanding to support more sophisticated patterns and agent structures.
• Building on this foundation, the same team developed LangGraph, which takes a more flexible approach through graph-based abstractions. By representing workflows as nodes (processing steps) and edges (relationships between steps), LangGraph provides lower-level primitives to make it easier for implementing a broad range of agent interaction patterns.

These patterns can be observed across other popular frameworks as well:

• Pydantic AI defines concepts like agent delegation and graph-based control flow that directly parallel our discussion of nested agency, where what appears as a tool from one perspective might contain an entire ABC cycle from another.
• OpenAI's Agent SDK similarly implements features like agent routing, agents-as-tools, and agent parallelization.
• Google's Agent Development Kit also defines sequential agents which executes one after another, parallel agents that executes in parallel, and loop agents that repeatedly executes until the trigger of a specific termination condition.

When choosing among these frameworks—or deciding whether to build your own system—consider the value horizons we discussed earlier. Higher-level frameworks accelerate development but may constrain flexibility. Lower-level frameworks require more code but offer greater control for building systems optimized for your specific domain.

Importantly, you don't always need to adopt an existing framework. Building directly on foundation models remains perfectly valid, especially for specialized applications where the complexity of existing frameworks outweighs their benefits. What matters most is understanding the interaction patterns that make agent systems effective, regardless of implementation approach.

The right system architecture creates a foundation upon which external integrations and human collaboration can build, enabling systems that scale gracefully as requirements evolve.

Cross-System Agent Communication: The Agent-to-Agent (A2A) Protocol

While the agent frameworks above generally excel at orchestrating agents within a system, there is also another challenge - how do independently developed agentic systems communicate with each other? This represents yet another level in our fractal view of agency—moving beyond intra-system communication to standardized inter-system agent dialogue.

Google's recently announced Agent-to-Agent (A2A) protocol addresses this challenge, providing a potential open standard for agent communication across organizational and technological boundaries. A2A's design principles make it distinctively suited for cross-system agent communication. For example, it establishes a common language for agents to discover capabilities, exchange information, and coordinate actions regardless of their underlying implementation. It enables agents to collaborate in their natural, unstructured modalities—even when they don't share memory, tools, or context. A2A is also built on universal standards like HTTP, SSE (Server-Sent Events), and JSON-RPC rather than introducing proprietary transport mechanisms.

A2A complements rather than replaces existing agent frameworks and protocols. Where frameworks like LangGraph and Pydantic AI handle intra-system orchestration and MCP (discussed in the next section) manages agent-to-tool/context communication, A2A focuses on cross system agent-to-agent dialogue.

World Integration: Connecting Agents with the Digital and Physical World

Connecting AI agents to the broader world beyond themselves encompasses everything from data sources and knowledge bases to APIs and services. Historically, this has been an area of fragmentation: custom integrations for each external system created considerable redundancy and maintenance challenges.

The Model Context Protocol (MCP) has emerged as a promising solution, defining a standardized way for AI systems to interact with external resources and tools. At its core, MCP defines three distinct types of context:

• User-controlled prompts: Instructions and templates that guide the model’s approach
• Application-controlled resources: Data and information that ground the model’s reasoning
• Model-controlled tools: Capabilities the model can discover and invoke when needed

Within the ABC framework, these "model-controlled tools" represent a critical bridge connecting the "Context" (the knowledge environment AI agents rely on) and the "Action" (the tasks AI can perform in the real world). While these tools expand the agent's accessible knowledge—enriching Context—they simultaneously enable execution of tasks, thereby transforming knowledge into Action. This dual nature explains why tool integration appears in both pillars of our framework: conceptually, tools extend an agent's capabilities (Action), while the standardized protocols for discovering and accessing them form part of the agent's understanding of its environment (Context).

The way MCP is structured makes it easier for prompt engineers, domain experts, and tool developers to work in parallel while maintaining appropriate security boundaries. Prompt engineers can optimize user instructions, domain experts can curate application resources, and tool developers can focus on capability implementation—all within a unified framework that maintains appropriate access controls.

For organizations building tool ecosystems, MCP's standardized discovery mechanism dramatically reduces integration friction. Rather than creating custom SDKs for each potential consumer of their tools, developers can implement a single MCP server that exposes their capabilities through a consistent interface. Any MCP-compatible agent can then discover and use these tools without custom integration code.

We are seeing a growing list of MCP servers being created, from browser tools, database connectors, code execution environments, and API gateways, creating an ecosystem of interoperable tools that any compatible agent can leverage. This standardization is particularly powerful when combined with the agent frameworks we discussed earlier, enabling more sophisticated orchestration patterns across diverse tools.

It's important to recognize that MCP is still an emerging standard with active refinement underway, security considerations also remain an area of ongoing development. Organizations implementing MCP should stay engaged with the community to adapt to these changes and contribute to the standard's maturation. Despite these growing pains, MCP's core architecture addresses fundamental needs not met by prior approaches, making it a worthwhile investment even as the details continue to evolve.

Process Execution: Transforming How Work Gets Done (Action)

The Action component of our ABC framework comes to life through process execution—how AI systems actually accomplish tasks and deliver results. As we already discussed in the “World Integration” section, standards like MCP play a crucial role here, allowing agents to seamlessly discover and invoke external tools. This creates the foundation for flexible, scalable execution that can evolve as new capabilities emerge.

Building on this foundation, this section focuses on the next critical aspect of process execution: transforming traditional workflows into approaches that fully leverage AI's unique capabilities.

The Transition from Human-Centric to AI-Native

Agentic AI systems are fundamentally objective-oriented, designed to autonomously achieve specific goals. When first implementing these systems, organizations naturally map them to existing workflows—processes that evolved in a pre-AI era to accommodate human cognitive patterns and organizational structures. This often involves creating specialized interfaces between AI and existing systems, such as APIs that connect models to structured knowledge bases.

While this approach provides a familiar starting point, it often constrains AI to paths optimized for human execution rather than leveraging AI's unique capabilities. In contrast, AI-native workflows begin with an understanding of AI’s strengths, aiming to reimagine how tasks are executed to maximize autonomy, efficiency, and scalability.

Identifying Opportunities for AI-Native Transformation

A more transformative approach proactively embraces new AI capabilities as they emerge, continually reassessing what's possible rather than remaining anchored to previous technical constraints. Consider document processing: traditional approaches required elaborate pipelines to convert PDFs into structured text, plus separate processing of unstructured components like charts and layouts. With advanced vision-language models, we can now directly process the original documents in one place, preserving their full informational richness. This shift doesn't just improve results—it fundamentally changes what's possible by eliminating artificial intermediary steps.

This principle applies across domains. Andrej Karpathy recently posted that today's web development resembles "assembling IKEA furniture"—developers must piece together numerous specialized components (frameworks, libraries, APIs) using complex toolchains. This approach evolved specifically to make development manageable for human teams which are typically constrained by cognitive complexity and context-switching.

As AI capabilities advance, we're beginning to see early explorations of alternatives. Rahul Sengottuvelu demonstrated a prototype email application built entirely through LLMs—with no traditional separation between frontend, backend, and database. Instead of manually coding each component, the system dynamically generates the appropriate UI elements and handles data operations through the LLM. While still experimental and not yet production-ready, this approach illustrates how we might reimagine development processes around AI's natural capabilities rather than human constraints.

Creating Adaptive Action Systems

The strategic imperative becomes clear: organizations must establish mechanisms to continuously identify and leverage emerging AI capabilities, rather than allowing established workflows to calcify. This means regularly questioning whether intermediate processing steps still add value or merely reflect historical technical limitations.

The most forward-thinking teams maintain a dual perspective—delivering immediate value through current approaches while systematically exploring how emerging capabilities might enable entirely new workflows. Looking further ahead, truly emergent AI-native workflows—where systems discover novel approaches through their own exploration—represent a frontier that builds upon this foundation of continuous adaptation.

As we consider the best practices for agentic AI systems today, this shift toward more AI-native workflows represents one of the most significant opportunities for creating lasting value. By systematically identifying and leveraging emerging capabilities, organizations can continuously unlock new possibilities rather than remaining constrained by workflows designed for previous technological eras.

Human-AI Synergy: Shaping the Evolution of Human-AI Partnerships

As we've explored agent orchestration and external tool integration, we now turn to the critical interface between humans and AI systems. The evolution of this relationship reflects a fundamental shift in how we collaborate with increasingly capable AI agents.

From Explicit Iteration to Apparent One-Shot Interaction

Human-AI interaction patterns have evolved dramatically. Early models required explicit decomposition of problems into small steps, with human oversight at each stage. This mirrored the sequential chains pattern we identified earlier—but with humans performing the orchestration role that agents now increasingly handle.

The introduction of Chain-of-Thought (CoT) prompting represented an important intermediate step, where we explicitly instructed models to reason step-by-step, but within a single generation. This approach still required carefully engineered prompts that guided the model through specific reasoning patterns.

Today's advanced reasoning models can internalize multi-step reasoning processes, allowing us to provide simpler, more direct instructions while the model handles complex decomposition implicitly. However, it is worth recognizing that this apparent shift to one-shot approaches is largely illusory. The iterative nature hasn't disappeared—it has simply been abstracted away at different levels:

• At the model level, these advanced reasoning models have effectively been trained to "bake in" iterative processes during inference. When we issue a single prompt, they perform multiple internal reasoning steps before producing their final output. The iteration still occurs, but happens implicitly within the model rather than through explicit back-and-forth exchanges.
• At the system level, our agentic architectures handle iteration through automated workflows that were previously managed manually. The various multi-agent patterns we discussed earlier all represent forms of iteration that happen behind the scenes, orchestrated by our systems rather than requiring direct human intervention.

Understanding these hidden iterations is essential for effective AI agent engineering. When our one-shot approaches succeed, they create the appearance of simplicity and immediacy. But when they fail—as they inevitably will in complex scenarios—we need to understand the underlying iterative processes to diagnose and address the issues effectively.

Context is King: The Human Role in Providing Relevant Information

A critical aspect of effective human-AI collaboration is the strategic provision of context. Even the most advanced reasoning models can perform dramatically better when provided with precise, relevant context. This isn't just about dumping information—it's also about humans exercising judgment about which context matters most.

For coding applications, providing relevant documentation, project context, and specific requirements significantly improves outcomes, even with cutting-edge models like OpenAI o1-pro or Gemini 2.5 Pro. This principle applies equally to agentic platforms like Windsurf, Cursor, Replit Agent and v0, where context quality directly correlates with output quality.

Emerging standards like Jeremy Howard's llms.txt proposal represent important steps toward systematizing context provision. By creating standardized formats for LLM-friendly content, these approaches make it easier to provide high-quality context consistently. However, we shouldn't underestimate the value of human curation in high-stakes scenarios. Taking time to select the most relevant context can dramatically improve results, particularly for that challenging final 10-15% of complex tasks that a one-shot AI approach typically struggles with.

The Human-AI Partnership: Finding the Right Balance

This brings us to perhaps another important takeaway: effective AI agent engineering isn't about eliminating human involvement but finding the optimal balance between autonomy and human guidance. This principle applies universally across all domains where AI agents operate, with coding and content creation serving as two of the prominent examples of a much broader transformation affecting industries and societies.

For substantive, insight-driven content creation, throwing a simple prompt at even the most advanced model rarely produces truly publication-quality results. While AI can generate adequate marketing copy or routine communications with minimal oversight, creating content with original insights, nuanced arguments, or domain expertise typically requires more human involvement. Deep research agents from platforms like Perplexity, OpenAI, Google, Anthropic, and X.ai improve outcomes by grounding content in sources from the Internet, but genuinely valuable thought leadership still mostly requires active human steering. The most effective approach often positions humans as "editors-in-chief" while treating models or agents as junior researchers or editing partners, collaboratively developing outlines and refining sections, step by step.

This insight connects to the heated ongoing discussion around "vibe coding"—where humans chat with LLMs to generate code without deeply engaging with the implementation details. As Simon Willison puts it, "If an LLM wrote every line of your code, but you've reviewed, tested, and understood it all, that's not vibe coding—that's using an LLM as a typing assistant." The same principle applies to content creation: effective collaboration on substantive content requires human understanding and direction at each step, not merely passive consumption of AI outputs through "vibe writing".

This perspective on human-AI collaboration brings us full circle to our ABC framework. The most effective agentic systems don't eliminate human creativity or judgment—they amplify it by handling routine aspects while enabling humans to focus on higher-level direction and quality control. This represents the essential promise of AI agent engineering: not replacing human capabilities but extending them in ways that create new possibilities.

The AGI Perspective

A critical perspective to keep in mind across all we have discussed above is the potential emergence of Artificial General Intelligence (AGI). Some argue that a sufficiently advanced AGI might render most of our current engineering efforts obsolete, as it could seamlessly organize knowledge, refine its own logic, and interact with the world with minimal instructions.

The history of chess provides an illuminating parallel to consider. In 1997, IBM's Deep Blue defeated world champion Garry Kasparov, marking a watershed moment in AI history. What followed was an era of "Advanced Chess" or "Centaur Chess," where human-machine teams competed against other human-machine teams. For several years, these centaur combinations proved stronger than either humans or machines alone. The human's strategic understanding complemented the computer's tactical calculation abilities, creating a superior composite intelligence.

Many believed this human-machine partnership represented the ultimate future—not just in chess, but in all domains where AI was applied. Yet by the mid-2010s, this narrative had collapsed. Chess engines like Stockfish and later AlphaZero became so powerful that even the strongest human-machine teams could no longer compete with machines alone. The raw computational power and sophisticated algorithms had advanced to a point where human input, once valued for strategic insight, became a limitation rather than an asset.

This chess trajectory suggests that while human-AI collaboration may be optimal today, future capabilities might eventually surpass even the most expertly engineered centaur systems. However, the timeline for such breakthroughs remains highly uncertain. AGI has been "just around the corner" for decades. Even though it appears to be closer than ever now given the rapid developments in LLMs, organizations cannot afford to simply wait for AGI to emerge before investing in AI capabilities—they must capture value today while preparing for an uncertain future.

This uncertainty reinforces the importance of the multi-horizon approach we've advocated throughout this article. These investments are not rendered worthless by the AGI consideration; they're essential bridges that create value now while building toward whatever future emerges. Among these investments, robust evaluation systems that enable continuous improvement stand out for their enduring value across all scenarios. Among all ABC components, the ability to systematically evaluate performance and incorporate feedback creates compounding returns over time. Even in a hypothetical AGI future, those with sophisticated evaluation frameworks would be best positioned to leverage these advanced capabilities effectively, with clear signals about where AGI systems excel, where they fall short, and how to deploy them for maximum impact.

Conclusions

Throughout this article, we’ve explored the evolution from isolated AI models to sophisticated agentic systems capable of delivering enduring value. The ABC framework has served as our guiding structure, clarifying how Brain, Context, and Action work together from individual agents all the way to complex multi-agent ecosystems.

We began by distinguishing between deterministic workflows and true agents with genuine autonomy. This emphasis on agency—the ability to make decisions and adapt on the fly—highlights which systems can thrive in complex, rapidly changing environments.

From there, we saw the fractal nature of the ABC framework, where what appears to be a single agent at one level might in fact be an entire ecosystem of specialized agents at another. This led us to orchestration patterns—sequential chains, hierarchical structures, collaborative networks, and competitive ensembles—that enable emergent capabilities beyond any single agent’s scope.

By examining AI agent engineering through multiple value horizons, we identified which choices depreciate quickly and which can appreciate over time. In the Brain domain, multi-agent cognitive architectures and orchestration usually outlast single model’s quirks. For Context, self-improving knowledge systems remain valuable no matter which retrieval technologies come and go. And for Action, standardizing tool interfaces and reimagining workflows deliver greater durability than narrow integrations tied to specific models.

We then turned to best practices, linking these principles to what real-world frameworks are already implementing. From modular agent design and standardized protocols like MCP to the shift toward AI-native workflows and hidden iterative processes, these approaches embody the ABC framework at scale.

Finally, we recognized that while AGI may change the game entirely, creating robust evaluation and feedback systems remains a timeless investment. These systems generate organizational knowledge that compounds in value across technological eras.

As you apply these insights—whether as an independent developer, a small business innovator, or part of a major enterprise—focus on building AI agent solutions that provide immediate wins while staying flexible enough to integrate the next wave of innovations. By balancing short-term pragmatism with a long-term vision, you can transition from fleeting novelty to sustainable impact, engineering agentic AI systems that remain invaluable long into the future.