The Anatomy of an Agent

At its core, an agent is a loop: observe, reason, act, repeat. But each of these three words conceals an entire subsystem. A production agent is not a loop. It is an architecture.

Perception Layer

The perception layer is everything between raw input and the agent’s working memory. For a coding agent, this is file contents, directory structure, error messages, and test output. For a clinical agent, it is free-text notes, structured lab values, medication lists, and prior encounters.

The common mistake is to dump everything into context and hope the model sorts it out. This works for small inputs and fails catastrophically for large ones. The perception layer must make decisions about:

• What to include — relevance filtering based on the current task
• How to structure it — schema design for the model’s consumption
• When to refresh it — cache invalidation and incremental updates

A well-designed perception layer transforms raw data into a context layer: a structured representation optimized for the reasoning module. The context layer is the contract between perception and reasoning. Get this contract wrong and everything downstream suffers.

Reasoning Loop

The reasoning loop is where the agent decides what to do next. The simplest form is a single-turn prompt: given the current state, what action should I take? This is rarely sufficient.

Production agents need multi-turn reasoning. The ReAct pattern — reasoning interleaved with acting — is the baseline. But ReAct has failure modes:

1. Looping: The agent oscillates between two states without making progress.
2. Tool fixation: The agent calls the same tool repeatedly with minor variations.
3. Premature termination: The agent declares success before the task is complete.

Each failure mode needs a guardrail. Looping is detected by state hashing. Tool fixation by action deduplication. Premature termination by an independent verifier or a stricter success criterion.

def react_loop(state, max_turns=20):
    for turn in range(max_turns):
        thought = reason(state)
        action = decide(thought)
        if is_terminal(action):
            return finalize(state)
        observation = execute(action)
        state = update(state, thought, action, observation)
        if detect_loop(state.history):
            return escalate(state)
    return timeout(state)

Action Interface

The action interface is the boundary between the agent and the external world. It defines what the agent can do and how those capabilities are exposed.

A good action interface is:

• Compositional: Complex tasks decompose into sequences of primitive actions
• Observable: Every action produces a structured result that the agent can reason about
• Reversible: Where possible, actions can be undone if they produce unwanted effects
• Idempotent: Running the same action twice produces the same outcome

The last point is critical for reliability. Non-idempotent actions (send email, charge credit card, deploy to production) require confirmation layers or human-in-the-loop gates.