AI Agent Architecture: How the Pieces Fit Together

The first time you watch a Claude agent chew through a real task — read a ticket, pull logs, patch a file, run the tests, and open a pull request — it can feel like magic. It is not. Underneath every competent agent is a fairly small set of moving parts wired together in a particular shape. Once you can see that shape, you stop guessing why an agent loops forever or forgets what it just did, and you start designing systems that behave. This post walks the whole architecture end to end: how a single user request becomes a sequence of model calls, tool invocations, and context updates, and where each part lives.

What an agent actually is, structurally

An AI agent is a control loop wrapped around a language model: the model proposes an action, an executor runs it, the result is fed back as new context, and the loop repeats until the model decides the task is done. That single sentence is the whole game. Everything else — tools, memory, planning, multi-agent fan-out — is an elaboration of that loop. With Claude, the model itself supplies the reasoning and the decision about what to do next; your harness supplies the loop, the tools, and the rules about when to stop.

It helps to separate the policy from the plumbing. The policy is Claude: given the current context, what should happen next? The plumbing is your code: how the request is framed, which tools are exposed, how results are captured, how errors are surfaced, and how the conversation is persisted. A lot of agent quality lives in the plumbing, because the model can only make good decisions over the context it is actually shown.

The seven components in the request path

Trace one request all the way through and you will pass through the same stations every time. There is an entry point that receives the user's goal. There is a context assembler that builds the prompt — system instructions, available tools, relevant history, and retrieved facts. There is the model call to Claude, which returns either a final answer or one or more tool-use requests. There is a tool executor that runs those requests against real systems. There is a result handler that turns raw output into a tool-result message. There is a memory layer that decides what survives into the next turn. And there is a termination check that decides whether to loop again or return.

flowchart TD
  A["User goal arrives at entry point"] --> B["Context assembler builds prompt"]
  B --> C["Claude reasons over context"]
  C --> D{"Tool use requested?"}
  D -->|No| E["Return final answer"]
  D -->|Yes| F["Tool executor runs the call"]
  F --> G["Result handler formats tool_result"]
  G --> H["Memory layer trims & persists context"]
  H --> B

The loop back from the memory layer to the context assembler is the part most newcomers underweight. Every cycle, the assembler re-decides what Claude sees. If you simply append everything forever, you blow the context window and dilute the signal; if you trim too aggressively, the agent forgets why it started. The architecture's health is mostly a function of how disciplined that re-assembly step is.

How Claude's tool-use turn actually works

When you give Claude tools, you are handing it a set of typed function signatures expressed as JSON schemas. On each turn Claude can respond with normal text, or it can emit a structured tool-use block naming a tool and supplying arguments that conform to your schema. Your harness pauses, executes that call, and returns a tool-result block keyed to the same id. Claude then continues as if the result had always been part of the conversation. This request–response handshake is the atomic unit of agentic behavior.

What makes this robust is that the contract is explicit and machine-checkable. Because arguments are schema-validated, you can reject malformed calls before they touch a real system. Because results come back as a discrete message, you can inject errors, truncation notices, or guidance right alongside the data. The Claude Agent SDK formalizes this loop so you do not hand-roll the message bookkeeping, but the mental model is the same whether you use the SDK, Claude Code, or a raw API integration.

Where state and memory live

Agents need two kinds of memory, and conflating them causes most architecture bugs. Working memory is the live conversation: the messages currently in the context window. It is fast, rich, and ephemeral — it vanishes when the window fills or the session ends. Durable memory is everything you deliberately write somewhere else: a scratchpad file, a database row, a vector store, a task tracker. The art is moving the right facts from working memory into durable memory before they scroll off, and pulling them back in only when relevant.

A practical pattern with Claude Code and similar harnesses is the externalized scratchpad: the agent writes its plan, decisions, and intermediate findings to a file, then re-reads that file on later turns. This keeps the live context lean while preserving continuity across a long task. Skills extend the same idea to procedural memory — folders of instructions and scripts Claude loads on demand — so the model does not carry every how-to in its head, only the ones the current step needs.

Orchestration: when one loop becomes many

Single-agent loops handle a surprising range of work, but some tasks are better split. An orchestrator agent can decompose a goal and spawn subagents, each with its own fresh context window and its own tools, then collect their results. This buys you parallelism and isolation — a research subagent's noisy intermediate context never pollutes the writer subagent. The cost is real: multi-agent runs typically consume several times more tokens than a single agent, because each subagent re-establishes its own context. Reach for it when the subtasks are genuinely independent and the parallel speedup or context isolation pays for the token bill.

Architecturally, an orchestrator is just another agent whose tools happen to be "spawn a subagent and wait for its summary." That recursion is the source of the pattern's power and its danger: without firm depth limits and clear subagent contracts, fan-out can explode. Treat each subagent boundary as an API — a tight input goal in, a structured summary out — and the system stays legible.

Common failure modes you can now name

With the architecture in view, the classic pathologies have obvious homes. Infinite loops are a broken termination check or a tool that never reports success. Hallucinated tool arguments are a context-assembler problem — Claude was not shown the schema clearly, or relevant prior results were trimmed. Forgetting earlier steps is a memory problem — working memory overflowed and nothing was persisted. Sluggish, expensive runs are usually an over-stuffed context being re-sent every turn. Knowing which station owns the bug is most of fixing it.

Frequently asked questions

What is the difference between an AI agent and a single LLM call?

A single LLM call maps one prompt to one response with no ability to act. An agent wraps the model in a loop with tools, so it can take an action, observe the result, and decide what to do next — repeating until the goal is met. The model is the same; the loop and tools are what make it an agent.

Do I need the Claude Agent SDK to build this architecture?

No. The architecture is provider-agnostic in shape, and you can implement the loop directly against the Claude API. The SDK and Claude Code save you from hand-writing the tool-call bookkeeping, context management, and subagent orchestration, which is worth a lot once your agent gets non-trivial — but the concepts are identical either way.

How big should the context window be in this design?

Bigger windows — Claude Code operates with up to a 1M-token context — give you headroom, but they are not a substitute for disciplined memory. The architecture should keep working memory lean regardless of window size, because every token in context is re-read on every turn and dilutes the model's focus. Use the large window for the rare turn that truly needs it, not as a dumping ground.

Bringing agentic AI to your phone lines

These same architectural pieces — a tight model loop, tool calls mid-task, and disciplined memory — are exactly what CallSphere runs on voice and chat, so its assistants answer every call, act during the conversation, and book real work around the clock. See it live at callsphere.ai.

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Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.