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Agent development is not a separate replacement for the software development lifecycle. It is a product-and-engineering loop that starts by deciding whether an agent is appropriate, continues through experiments, implementation, testing and controlled release, then uses production evidence to guide the next iteration. Evaluation, risk controls and feedback belong throughout that loop—not only at launch.
What is the agent development lifecycle?
The agent development lifecycle describes the work needed to determine whether an AI agent is suitable, build and release it responsibly, and maintain it as real needs and operating conditions change. An agent can use tools to take actions rather than only produce text, so the work includes decisions about permissions, boundaries, reliability and oversight as well as model and software choices. NIST describes this tool-using capability in its 2025 workshop report on tool use in agent systems.
There is no single mandatory set of phase names. Microsoft’s guidance presents five phases—discovery, experimentation, build, deploy and operational steady state—and notes that phases may overlap and iterate. LangChain, describing its own practice, uses a four-part build, test, deploy and monitor loop. These are useful operating models, not a universal standard.
Where does agent development fit in the software development lifecycle?
It fits across product discovery, engineering delivery and operations. Discovery and experimentation help establish whether an agent solves the right problem and under what conditions. Build and test are part of implementation and verification. Deployment is a controlled transition into production; monitoring and improvement then feed operational evidence back into the next development cycle. This mapping is a synthesis of the Microsoft and LangChain frameworks, not a prescribed industry taxonomy.
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The distinction matters because an agent is not finished when its prompt or model is selected. A release can change how users, data, tools and external services interact. The team therefore needs ways to evaluate behavior before release, observe it in use, investigate failures and adjust the system safely.
What are the stages of building and deploying an AI agent?
1. Discovery: establish the need and boundaries
Start with the business or user need, not with a desire to add an agent. Identify stakeholders, requirements, responsibilities and the scope of actions. Decide what should remain out of scope and whether the expected value justifies the extra complexity compared with a conventional workflow or software feature. Microsoft’s enterprise agent design guidance recommends assessing whether an agent is warranted and using clear charters and boundaries.
- Define the outcome the agent is meant to support and how people will judge whether it works.
- Specify the information and tools it may use, and actions it must not take.
- Identify where human review, escalation or a deterministic workflow is required.
2. Experimentation: test the premise under representative conditions
Use experiments to test hypotheses about the task, model, tools and expected responses before committing to a production design. Microsoft advises using real-world datasets and current models; a proof of concept based on synthetic or limited data can give a misleading impression of performance. Keep the gap between experimentation and build small where possible, since changes in data or models can make early results less representative.
Record cases that work, cases that fail and the conditions that distinguish them. These findings provide a practical starting point for build requirements and evaluation, rather than relying on a few attractive demonstrations.
3. Build: turn findings into a controlled system
Build the production-ready solution around more than a model call. Architecture, orchestration, instructions, tools and boundaries all affect reliability and maintenance. Microsoft’s enterprise guidance recommends approved orchestration patterns, deterministic workflows for critical business logic, version-controlled instructions and validation before deployment. The design should make it possible to understand and change the agent without losing track of which instructions, tools or configuration produced a given behavior.
How much to build yourself depends on the team’s context. Microsoft says managed orchestration can speed deployment and provide built-in security, but may limit customization; code-first frameworks can offer more granular control while requiring significant engineering investment and ongoing maintenance. Compare the options against your workload, team skills, risk tolerance and platform constraints rather than treating one framework as universally best.
4. Test and evaluate: establish evidence before release
Evaluate the agent against representative tasks and known failure cases before it reaches production. Testing should cover whether it achieves the intended outcome, stays within its scope, uses tools appropriately and hands off when a person or deterministic process is needed. LangChain’s vendor-authored lifecycle emphasizes that testing begins before production, not only after problems appear.
Evaluation is not a one-time gate. Preserve useful test cases and add cases that arise from observed errors or changing requirements. This makes comparisons between versions more meaningful and helps teams catch regressions before deployment.
5. Deploy: move into production with controls
Deployment is a transition, not proof that the agent will behave identically in every live situation. Microsoft describes this phase as moving the solution into production while seeking to preserve the quality and performance established in testing. Use the release process to validate the deployed configuration, including instructions, integrations, access permissions and the route for human intervention.
Tool risk depends on what a tool can access and do in its particular deployment. NIST’s workshop report highlights functionality, external access, write permissions, potential harm, reversibility, reliability, observability and autonomy as useful dimensions for reasoning about tool use. A read-only lookup has a different impact from an irreversible action that changes a record or affects a person; consequential actions may warrant narrower permissions or human review.
6. Operational steady state: monitor and improve
After release, observe behavior and outcomes, review feedback and investigate recurring failures. Monitoring can reveal edge cases that were absent from pre-release tests; use those observations to refine evaluations and the next build. LangChain presents this as the connection between monitoring and a subsequent build-and-test cycle. Microsoft’s lifecycle likewise treats operational steady state as ongoing maintenance and optimization, not a final endpoint.
Operational practice should account for visibility, debugging, evaluation, versioning and safe changes. Traces and outcome records can help teams understand what happened, while a controlled update process helps them determine whether a change improved behavior or introduced a regression.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteHow should governance and evaluation span the lifecycle?
Governance is not a stage to complete once and set aside. LangChain explicitly frames governance as sitting around its lifecycle. In practice, the controls chosen during discovery should inform experiments, build decisions, release permissions and operational review. Evaluation should also persist: use it to assess versions before deployment and to incorporate relevant failures and new requirements afterward.
- At discovery: define responsibility, scope, prohibited actions and escalation paths.
- During experimentation and build: use representative data and validate the behavior of instructions, orchestration and tools.
- At release: verify access and write permissions, observability, review points and recovery options for the deployment.
- In operation: examine traces, outcomes, feedback and recurring failures, then feed appropriate cases into evaluation and the next iteration.
These practices do not eliminate uncertainty. They make assumptions and changes easier to inspect, and provide a disciplined route from observed behavior to a safer revision.
Is the agent development lifecycle a finished standard?
No. Microsoft’s five-phase lifecycle is official Microsoft guidance, and LangChain’s four-part ADLC is a vendor’s account of its own practice; neither should be mistaken for a regulatory or universal standard. NIST’s 2025 report discusses lessons and tool-use considerations from a workshop rather than defining an end-to-end development lifecycle.
NIST announced its AI Agent Standards Initiative in February 2026, covering standards, open protocols, and security and identity research, with additional deliverables to follow. The announcement describes an initiative in progress, not a completed lifecycle standard. Teams can use current lifecycle models as practical guides while keeping their own controls aligned with the systems and risks they operate.
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