The Tool Desk
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Microsoft Learn describes these five phases in its agent development lifecycle. The phases are iterative: evidence from testing and operation can send a team back to an earlier phase when assumptions, requirements, or conditions change.
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What should each phase produce?
| Phase | Central question | Evidence to carry forward |
|---|---|---|
| Discovery | Is an agent a suitable way to address a valuable, bounded need? | Defined objectives, users, scope, assumptions, requirements, and relevant data characteristics. |
| Experimentation | Do the riskiest assumptions hold with representative data and current models? | Recorded test results, limitations, and evidence about whether the proposed approach merits building. |
| Build | Can the solution be made reliable and maintainable for its intended use? | A production-oriented design, implemented system, and planned tests and failure handling. |
| Deploy | Does the integrated system meet its requirements in its actual operating context? | Validation of integration, user experience, performance, and applicable legal or compliance needs. |
| Operational steady state | Does the agent remain useful, safe, and supportable as conditions change? | Monitoring, evaluation, incident records, remediation, and decisions to continue, revise, or retire the system. |
1. Discovery: decide whether an agent is warranted
Begin with the need, not with a preferred model or agent framework. An agent adds complexity, so establish that its expected value justifies that complexity and define a scope narrow enough to evaluate. NIST’s AI Risk Management Framework 1.0 assigns fit-for-purpose design responsibilities across relevant AI actors, including identifying context, objectives, assumptions, requirements, and data characteristics.
- Describe the user or business need and the outcome that would count as useful.
- Identify intended users, affected people, stakeholders, and accountable business ownership.
- Document the context of use, assumptions, requirements, and relevant data characteristics.
- Set a bounded scope, including what the agent is not meant to do and where human judgment remains necessary.
- Consider the agent’s intended tools, data access, integrations, autonomy, and possible impact; use these to shape later controls.
Discovery should leave the team with a testable problem statement, not a presumption that an agent must be built. If the need or scope is not clear enough to evaluate, refine it before proceeding.
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2. Experimentation: test the riskiest assumptions
Use experiments to find out whether the proposed approach works under conditions resembling the intended use. Microsoft recommends evaluating agent responses with representative real-world data and current models; synthetic-only or limited test data can make proof-of-concept behavior less likely to carry over to production. This is a risk-reduction practice, not a guarantee of production quality.
- List assumptions that could invalidate the design, such as whether the agent can use available information correctly or whether a proposed tool can support the task.
- Test those assumptions with representative examples, including relevant variations in inputs and context.
- Record what was tested, which model and data were used, what worked or failed, and the limits of the results.
- Keep experimentation close to build so model or data changes have less time to make findings stale.
Do not treat a successful demonstration as evidence that the system is ready for real users. Experiments help determine whether to proceed and what needs to change; later validation must still address the built and integrated system.
3. Build: turn evidence into a maintainable system
Translate the evidence and requirements into a production-oriented design. NIST places testing and validation within development and notes that tests can be planned as early as design. Specify how the agent is allowed to act in the actual use case rather than relying on a generic definition of an “agent.”
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- Define tools, data sources, system integrations, permissions, and the boundaries of access.
- Design for reliability and maintenance, including how errors are handled and how the system can be changed when models, data, or requirements evolve.
- Decide where the agent should stop, request clarification, or hand work to a person.
- Plan tests against stated requirements and risks, including cases where the agent’s expected behavior is uncertain or a dependency fails.
- Preserve the connection between requirements, tests, results, and the design decisions they support.
The architecture should reflect the specific task and consequences of failure. A workflow with limited access and low impact does not call for the same safeguards as one able to affect external systems or people.
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Deployment is more than making the agent available. Validate that the integrated system retains the quality and performance characteristics established during experimentation and works for its intended users and environment. Microsoft’s lifecycle guidance includes integration compatibility, user experience, and relevant legal or compliance review in deployment validation.
- Check that integrations, data access, permissions, and the deployment environment match the intended design.
- Evaluate the user experience and whether users can understand the agent’s role and reach a human when needed.
- Complete applicable legal and compliance reviews for the use case and operating context.
- For actions that can affect external systems or people, define approval and escalation rules before enabling those actions.
No universal approval threshold or autonomy limit is established by the cited frameworks. Accountable teams need to set rules appropriate to the agent’s access, autonomy, and impact, and make those rules part of deployment validation.
5. Operate: monitor, respond, and improve
In operational steady state, continue maintaining, monitoring, evaluating, and adjusting the agent as business needs, models, and data evolve. NIST’s AI RMF treats risk management as lifecycle work and describes ongoing testing, incident tracking, and remediation—not just pre-release checks.
- Assign an owner for operational health and clarify who responds to incidents, errors, and user concerns.
- Track incidents and errors, investigate their causes, and record remediation.
- Periodically test and recalibrate the system against its intended use and current conditions.
- Maintain a response and redress process for people affected by the system’s outputs or actions.
- Feed operational findings back into discovery, experimentation, build, or deployment when the system’s assumptions or requirements need revision.
When to revise or retire an agent
Operational review should lead to a deliberate decision: continue operating, revise the system, or retire it. Revisit discovery when the business need or intended users change; return to experimentation or build when evidence reveals that the approach or implementation needs work; and reassess deployment when integrations or operating conditions change. If the need no longer justifies the system or it cannot be supported within appropriate controls, retirement is a lifecycle outcome rather than a reason to keep an unsuitable agent in service.
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Test, evaluation, verification, and validation (TEVV) belong throughout the lifecycle. The checks differ by phase: discovery tests whether the problem and assumptions are well framed; experimentation tests proposed approaches and data; build tests the implemented system against requirements; deployment validates integration and context; operation checks ongoing performance, incidents, and impacts.
For claims made by an agent, useful evaluation questions include whether the source supports the claim (faithfulness), whether the response preserves the source’s full message (completeness), and whether the evidence is strong enough to carry the claim (sufficiency). NIST’s Building Evaluation Probes into Agentic AI project describes probes for testing factual grounding against a human-curated corpus and creating machine-readable evidence trails. The project is ongoing; these dimensions are useful evaluation concepts, not a settled universal benchmark.
Keep evaluation evidence connected to the decision it informs. A result should be interpretable in light of the tested model, data, conditions, and intended use; otherwise, it is difficult to tell what the result establishes or whether it remains relevant after a change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Assign governance and accountability across the lifecycle
Governance is the work of making responsibilities and decisions clear. NIST’s AI RMF describes multiple AI actor groups and the value of diverse perspectives. OpenAI’s Practices for Governing Agentic AI Systems offers initial practices for safe and accountable operations while identifying unresolved questions about how to operationalize them. Neither source is a single mandatory lifecycle standard or a substitute for an organization-specific policy.
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- Business owners: define the need, intended outcomes, and acceptable use.
- Developers: design and build the system, implement controls, and connect tests to requirements.
- Platform operators: manage deployment and operational capabilities, including the relevant model, orchestration, and integration environment.
- Evaluators: assess whether evidence supports the system’s intended behavior and remains relevant over time.
- Governance and compliance roles: help determine applicable obligations, risk decisions, and review responsibilities.
Make decision rights explicit, especially for changes in access, autonomy, intended use, or impact. The particular approval gates, risk thresholds, service levels, and retention periods must be determined for the organization and context; the cited frameworks do not prescribe universal values for them.
Choose platforms by lifecycle fit, not by a universal ranking
Platform capabilities shape orchestration, model access, and operational features, as Microsoft notes. Assess the platform against the whole lifecycle rather than selecting on model access alone.
- Use-case fit: can it support the intended task and boundaries?
- Model access: does it provide access appropriate to the solution’s needs?
- Orchestration: can it coordinate the required agent workflow and tools?
- Data and system integration: can it connect to needed sources while respecting the defined access boundaries?
- Operations: does it support the operational work the team must perform?
- Evaluation and observability: can the team inspect behavior and gather evidence relevant to its tests?
- Governance controls: can the team implement the permissions, approvals, and oversight its use case requires?
- Deployment environment and maintenance: does it fit where the system must run and what the team can support over time?
There is no best platform established for every agent. The useful choice is the one that meets the system’s requirements without creating an operational or maintenance burden the team cannot manage.
Use frameworks as inputs to policy, not as a substitute for it
NIST AI RMF 1.0 and product lifecycle guidance provide useful structures, but they do not set an organization’s complete operating policy. NIST CAISSI’s Guidelines page, updated 2026-09-30, includes initial public draft material on benchmark evaluation; treat draft guidance as draft and check its status before relying on it. Teams still need to define context-specific decision rights, controls, and operational procedures.
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