My experiments with AI

A software engineers journal

Building Agents That Last: The Anatomy of Scalable AI Systems

When we talk about AI agents, it helps to think in human terms. A useful agent system needs a skeleton, hands, a mind, a brain, a library, a body, guardrails, and skin.
That’s not poetry — it’s the anatomy of every serious agent framework being built today. And the leaders across Anthropic, OpenAI, Google, AWS, Microsoft, Databricks and Scale AI all circle back to these same eight building blocks.

🦴 Skeleton: Agentic Frameworks

This is the backbone — the loop or graph that defines how the agent perceives, decides, and acts. Agentic frameworks refer to the overall architectural approach and design patterns that enable autonomy in AI agents. These frameworks provide the scaffolding for LLM-based agents to perceive, decide, and act in a goal-directed manner, often abstracting the “LLM + tools + control loop” pattern into a cohesive system. Key considerations include whether the framework supports single-agent or multi-agent setups, synchronous vs. event-driven execution, and how much logic is handled by the framework versus left to developers.

  • OpenAI Agents SDK and A Practical Guide to Building Agents emphasize starting with a single agent and expanding only when necessary.
  • Anthropic highlights orchestration patterns like prompt chaining, routing, orchestrator–workers, and evaluator–optimizers.
  • LangGraph (part of LangChain) brings stateful, durable orchestration for open-source builders.
  • Google ADK Google’s Agent Development Kit (ADK) is a flexible and modular framework that defines workflows using workflow agents (Sequential, Parallel, Loop) for predictable pipelines, or leverage LLM-driven dynamic routing for adaptive behavior.
  • CrewAI and Microsoft AutoGen both shine in multi-agent coordination.
  • Alibaba AgentScope supports distributed, large-scale multi-agent simulations.

✋ Hands: Tool Integration

Tools are how agents act in the world. Tool integration enables agents to extend beyond text generation – using external APIs, databases, web browsers, code execution, etc. All major platforms emphasize three categories of tools:

  • Data Tools: Retrieve context and information (queries, document reading, web search)
  • Action Tools: Interact with systems (emails, database updates, API calls)
  • Orchestration Tools: Agents serving as tools for other agents

Tool definitions and specifications should be given just as much prompt engineering attention as your overall prompts. Put yourself in the model’s shoes. Is it obvious how to use this tool, based on the description and parameters, or would you need to think carefully about it?

🧠 Mind: Memory Systems

Agents without memory are goldfish. Memory systems allow agents to retain and recall information beyond a single turn. Most implementations rely on:

  • Conversation History: Built into LLM context windows
  • External Vector Stores: For long-term information retrieval
  • Session State: Temporary data during agent execution
  • Tool Results Cache: Storing intermediate outputs

None of the major platforms have established definitive standards for persistent agent memory or long-term learning capabilities.

🧩 Brain: Reasoning Frameworks

Reasoning frameworks refer to the methods an agent uses to break down problems, plan actions, and make decisions step-by-step. This includes prompt strategies (chain-of-thought prompting, self-reflection), workflow patterns (loops, conditionals, parallel tasks), and multi-agent coordination strategies that enhance reasoning. This is the decision-making core — how the agent plans and adapts.

Reasoning Approaches

  • Anthropic outlines evaluator–optimizer, parallelization, and orchestrator–worker loops.
  • Google ADK formalizes Sequential, Parallel, and Loop reasoning nodes.
  • AWS Strands SDK – provides orchestration of reasoning as explicit, reproducible flows across models and tools, emphasizing enterprise-grade workflows with checkpoints, retries, and dependencies rather than emergent agent chats.
  • Scale AI uses workflows and state machines for structured agent reasoning.
  • Microsoft AutoGen enables multi-agent debate and collaboration

Reasoning Pattern Implementation

  1. Step-by-Step Decomposition: Breaking complex tasks into manageable steps
  2. Environmental Feedback Loops: Continuous validation against real-world results
  3. Error Recovery: Graceful handling of failures with human handoff capabilities
  4. Context-Sensitive Adaptation: Dynamic adjustment based on environmental changes

📚 Library: Knowledge Base

Agents need more than memory — they need access to live, grounded knowledge. In agent systems, the knowledge base refers to the external domain knowledge the agent can draw upon – documents, enterprise data, factual databases, etc. Effective Knowledge Integration means the agent can retrieve and utilize relevant information from large data stores when needed (often via retrieval-augmented generation). All platforms implement some form of RAG, but with different emphasis:

🏃 Body: Execution Engine

The execution engine is the runtime environment that runs the agent’s code, manages its lifecycle (start, pause, resume, stop), and provides the compute, isolation, and scaling needed. This is where the agent lives and breathes.

  • AWS AgentCore Runtime supports isolated sessions and long-running tasks (up to 8 hours).
  • Vertex AI Agent Engine provides managed runtime and debugging UI.
  • LangGraph enables checkpoints and recovery for OSS environments.
  • AgentScope and AutoGen emphasize distributed, event-driven messaging.
  • Google ADK containerizes and deploys agents anywhere – run locally, scale with Vertex AI Agent Engine, or integrate into custom infrastructure using Cloud Run or Docker.

🛡️ Guardrails: Monitoring & Governance

Without governance, agents are liabilities. Think of guardrails as a layered defense mechanism. While a single one is unlikely to provide sufficient protection, using multiple, specialized guardrails together creates more resilient agents.

👤 Skin: Deployment & Interfaces

This is how users actually meet the agent.  This aspect covers how agents are deployed to end-users or integrated into applications – including user interfaces (chat UI, voice, etc.), APIs, and the overall environment in which users interact with the agent.

With all eight building blocks in place, the question shifts from what agents are made of to how they should be designed and when they should be used.

🛠 Design Guidance from Multiple Sources

When you strip back the metaphors and blueprints, the real test of any agent system is in how it’s designed. Interestingly, the leading players — OpenAI, Anthropic, Google, Databricks, MIT and Scale AI — are converging on the same fundamental best practices:

  • Start small and focused. OpenAI recommends beginning with a single agent running a loop with an exit condition before splitting into multi-agent systems. Anthropic echoes this: start with simple workflows like chaining or routing, graduate to agents only when the task is ambiguous and open-ended.
  • Treat agents like software components. Google’s ADK frames agents as modular units with clear APIs and versioning. Compose them through A2A protocol for multi-agent collaboration, and enforce safety and sandboxing at the interface layer.
  • Design for modularity. Databricks stresses that compound AI systems must mix deterministic modules (retrieval, typed tools, structured logic) with probabilistic reasoning (LLMs). This separation increases reliability and compliance — especially in regulated industries.
  • Build in guardrails. OpenAI and AWS emphasize layered safety: input filters, tool-level authorization, output validation, and detailed logging. Google ADK adds callback hooks and built-in sandboxing to protect against prompt injection and misaligned instructions.
  • Iterate, evaluate, improve. MIT’s Playbook and Scale AI all converge on the same theme: avoid big-bang rewrites. Instead, prototype with small agents, measure impact against clear metrics, then expand gradually.

✨ My takeaway

The frameworks may differ in branding, but the principles are universal:

  1. Keep agents small and focused.
  2. Wrap tools in deterministic, well-typed interfaces.
  3. Use a single agent loop before scaling out.
  4. Modularize retrieval, reasoning, and governance.
  5. Protect every layer with guardrails.
  6. Treat design as iterative — test, refine, expand.

These design patterns are what make agents not just demos, but production-ready systems.

Closing Reflection

Thinking of agents as living systems helps clarify the challenge. The skeleton, hands, mind, brain, library, body, guardrails, and skin show us what an agent is made of. But anatomy alone isn’t enough — design choices determine whether those systems perform in the real world.

From OpenAI’s emphasis on starting small, to Anthropic’s patterns of orchestration, to Google’s ADK modularity, Databricks’s separation of deterministic vs probabilistic flows and Scale AI’s eval-driven iteration — the message converges: treat agents like serious software components, built iteratively with clear boundaries, governance, and observability.

And not every problem even needs an agent. As Anthropic reminds us, deterministic flows excel when tasks are predictable, repeatable, and tightly scoped. Agents should be reserved for open-ended, unpredictable triage where multiple hypotheses must be explored. The art lies in knowing when to scaffold with deterministic code and when to unleash the flexibility of agents — often combining both in the same system.

The real craft of building agents that last is therefore threefold:

  1. Understand the anatomy. Know the essential building blocks.
  2. Apply design discipline. Modularize, guard, and iterate as you would with any critical system.
  3. Choose wisely. Use agents where ambiguity and adaptability matter; use deterministic flows where precision and predictability are paramount.

In that balance lies the difference between flashy prototypes and durable, production-ready agents.

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My Experiments with AI is where I explore the cutting edge of artificial intelligence through hands-on experimentation and thoughtful analysis.