To productize an AI agent, define the job and its boundaries before choosing the model. A durable agent product has a clear user, trigger, outcome, tool set, approval policy, service level, evaluation suite, and fallback. The goal is not maximum autonomy on day one; it is reliable ownership of a valuable slice of work.
When is an agent the right product?
Use an agent when work requires judgement across several steps, unstructured information, and tool use—and when the result is valuable enough to justify monitoring and exceptions. Use deterministic software for stable rules. Use a copilot when the human should compose or decide every time. Use an agent when the system can responsibly own a bounded outcome.
OpenAI’s practical guide to building agents describes model, tools, and instructions as core components. Anthropic’s Building Effective Agents distinguishes predefined workflows from systems where models dynamically direct their process. That distinction is a product decision, not merely a technical one.
What is the agent’s job contract?
Write a one-page contract: “When this trigger occurs, for this user, the agent will produce this outcome, using these systems, within these limits; it will ask for help when these conditions occur.” Add prohibited actions and the evidence users need to trust the outcome. If the contract cannot be stated plainly, the boundary is too broad.
How do you choose the first workflow?
Score candidates on frequency, business value, variability, data readiness, reversibility, evaluation clarity, integration effort, and risk. A good first workflow is repetitive but not rigid, has a visible outcome, and allows mistakes to be caught before harm. Avoid beginning with the organisation’s most politically complex or irreversible decision.
What should the first architecture include?
- A simple orchestration loop and the smallest useful tool set.
- Authenticated user and workload identity.
- Structured tool inputs and deterministic validation.
- State that is inspectable, scoped, and recoverable.
- Approval gates for consequential actions.
- Tracing, cost controls, retry limits, and a kill switch.
- A queue or human handoff when confidence or policy thresholds fail.
Start with one capable agent and clear tools. Add multiple agents only when specialisation, permissions, or parallel work creates measurable value. Complexity is not a product benefit unless users experience a better outcome.
How does an agent earn autonomy?
Use an autonomy ladder: observe human work; recommend; draft; execute with approval; execute within limits with sampled review; then handle the bounded workflow automatically. Promotion requires evaluation and production evidence at each level. Move an agent down when a model, tool, policy, or environment changes.
What makes users trust an agent?
Show what happened, what sources were used, what action is proposed or completed, and how to correct it. Calibrate uncertainty instead of hiding it. Preserve an audit trail. Make escalation faster than redoing the task. Trust grows from controllability and consistent outcomes, not from giving the agent a human name.
How should the MVP be measured?
Measure completed outcomes, acceptance without correction, resolution time, exception rate, harmful errors, human review time, tool failures, latency, and unit economics. Compare against the current process. A high model score with a low end-to-end completion rate is not a successful product.
What is the launch rule?
Launch narrowly when the agent can complete the job contract for a defined cohort, failures are observable and recoverable, users understand the boundary, and unit economics improve with use. Cognis applies this discipline to its own AI products and systems delivered through agent engineering.