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Five rules for building an AI agent that works

Five principles from Anthropic's agent guide: start simple, route, check ground truth, design tools, set limits.

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An agent can spin for hours and fix nothing. The five rules below come from Anthropic's "Building effective agents" guide, published December 2024 [1]. The guide covers when to use agents, when to skip them, and how to make them reliable when you do need them.

The story in the reel is fictional (Idris, Hamdan, and Bolt are made up). The rules are real.

Start with one model call

Try one plain model call with good context before you add orchestration. Agentic systems trade latency and cost for performance, and many tasks don't need that trade-off [1].

A single LLM call with retrieval and good examples is often enough. Add loops and tools only when they clearly help.

Route easy work to a small model

Send easy requests to a small, cheap model. Send hard ones to a stronger model. Anthropic calls this the routing pattern [1].

You can route by classification: a fast model decides which tier handles the request, and each tier has a prompt tuned for its difficulty level.

Make the agent read real results

After every step, make the agent read the actual output of a tool or test, not its own guess about what it did. Anthropic calls this ground truth [1].

If the agent claims a fix without running the test, it has no evidence. Make it read the test output and keep going until the result is what you asked for.

Write tool descriptions like notes to a teammate

Write each tool's description as if you were onboarding a new teammate. Include examples, edge cases, and what the tool cannot do. Require absolute file paths so the agent doesn't get lost [1].

Anthropic found that better tool documentation removed a whole class of mistakes. The work you put into the tool interface pays off across every call.

Set limits and pause for a human

Set a maximum number of steps. Pause for a human before anything risky: deployments, deletions, or anything with side effects that can't be undone [1].

The agent's autonomy is useful, but unchecked loops can burn tokens or cause real damage. Limits and checkpoints keep the scope under control.

Your agent now has structure: one call first, routing by difficulty, ground truth after every step, clear tool docs, and hard limits on what it can do alone.

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By Full Byte Developer

Filed under AI agents

Questions about this guide

Do these rules apply to any agent framework?

Yes. They are design principles that apply whether you're using the Claude Agent SDK, LangChain, or building from scratch.

What is ground truth in this context?

Ground truth means the actual output of a tool or test, not the agent's guess about what it did. Making the agent read real results after every step catches errors early.

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