Start with a workflow, not a model

A model is only one component. A production agent also needs instructions, authorized tools, business context, state, validation, error handling, observability, and a clear way to hand work back to a person. Begin by mapping the job as it happens today: trigger, inputs, decisions, systems touched, exceptions, and final approval.

Good candidates are frequent, text- or data-heavy, and currently slowed by switching between systems. Examples include classifying inbound requests, preparing order exceptions for review, enriching product records, summarizing support histories, or drafting a response from approved knowledge.

Choose the smallest useful autonomy level

Not every workflow needs an autonomous agent. A deterministic integration is often better when the rules are stable. Retrieval plus a model may be enough when the job is to answer from a controlled knowledge base. Use an agent when the task genuinely requires planning, tool selection, and adaptation across several steps.

A useful first release can prepare an action without executing it. The agent gathers context, proposes the next step, and a person approves. This preserves speed while exposing edge cases safely. Autonomy can expand only after evaluations show where the system succeeds and where it fails.

  • Low risk: summarize, categorize, extract, draft.
  • Medium risk: update internal records with validation and an audit trail.
  • High risk: move money, make legal commitments, delete data, or contact customers without review.

Design reliability into the system

Reliable agents operate within boundaries. Give tools narrow permissions, validate structured outputs, cap retries and cost, and record each consequential action. Sensitive data should be minimized before it reaches a model. High-impact actions need explicit confirmation or policy checks.

Create an evaluation set from real examples before launch. Include routine cases, incomplete inputs, conflicting instructions, and adversarial content. Measure task completion and business correctness—not whether the response sounds confident. Logs and traces then help the team investigate failures and improve prompts, tools, or workflow design.

Pick technology after defining the operating model

OpenAI, Anthropic, and Google each provide strong models and agent-building capabilities. The decision should follow requirements for tool use, latency, data handling, model quality, cost, and the environments your team already operates. Avoid coupling every business rule directly to one model response.

A well-designed agent has replaceable layers: model access, tools, knowledge, policy, and interface. That architecture makes it possible to evaluate new models without rebuilding the entire product and keeps critical business logic testable outside the model.

Sources & further reading