Building an AI agent starts by removing ambiguity. Before choosing a model or framework, describe a task with an observable input, decision, action and outcome. If the team cannot say when the task is correctly complete, the agent has no reliable success condition.

Minimum architecture

A first version needs a model, instructions, one or a few tools, run state, limits, logs and a test set. Start with one agent. Add specialists only when responsibilities or permission boundaries genuinely require separation.

Tool contracts

Tools should accept structured inputs and return unambiguous success or failure. Prefer narrow operations such as “look up order” or “prepare refund”, with a separate approval gate for “confirm refund”.

Evaluation before endless prompt tuning

An initial set of 30 to 50 representative cases teaches more than tuning instructions without a reference. Define expected outcomes, allowed actions, required evidence and failure severity. Run it whenever a model, instruction, tool or data source changes.

Progressive launch

Observe the human process first, then generate suggestions without execution. Next, allow reversible low-impact actions. Increase autonomy only when quality, cost, escalation and incidents remain within defined limits.

Use the AI agent evaluation template to record criteria and the complete AI agents guide to choose an architecture.

How to do it

  1. Define the outcome

    Choose a bounded task, evidence of completion and the conditions that should stop or escalate the run.

  2. Choose tools

    Expose only required operations with validated parameters, least privilege, timeouts and clear errors.

  3. Design context and state

    Separate run data from persistent memory and define origin, freshness, retention and correction.

  4. Add limits and approvals

    Define prohibited actions, step and cost budgets, and human approval before sensitive operations.

  5. Create an evaluation set

    Build normal, ambiguous, incomplete, adversarial and prohibited cases with expected outcomes.

  6. Test failures

    Simulate unavailable tools, invalid responses, malicious data, repeated actions and missing context.

  7. Launch gradually

    Start in suggestion mode or with a small cohort, compare against a baseline and expand with evidence.

  8. Monitor and version

    Record outcomes, cost, latency, tools, escalations and the versions of models, instructions and integrations.

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AI Agents

Understand, build, evaluate and operate AI agents with clear goals and boundaries.

PILLAR GUIDEAI Agents: what they are, how they work and when to use themLearn how AI agents use tools, state, guardrails and evaluations, and decide when agentic systems make sense for products and operations.RELATED GUIDEAI agents with n8n: architecture and production checklistBuild AI agents with n8n using triggers, tools, optional memory, human approval, evaluations and explicit error handling.RELATED GUIDEAI agent examples: use cases, metrics and risksExplore AI agent examples for product, support, operations and engineering, including tools, outcomes, metrics and risks.RELATED GUIDEAI agent evaluation templateEvaluate AI agents for task completion, tools, safety, cost, latency, oversight and readiness to launch.

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