The right specification for agentic development is not the longest document. It is the smallest clear contract that gives an AI coding agent a concrete goal, examples of correct behavior, explicit non-goals, and checks that can verify the result.
This matters because AI coding agents make implementation faster, but they do not decide what “correct” means for your product. Recent developer research shows that coding agents are becoming a regular part of professional workflows, while current guidance on agentic development emphasizes structured intent and executable verification rather than vague prompts. JetBrains Research reports that 90% of surveyed professional developers used AI coding agents at work at least weekly in May–July 2026, and a recent Stack Overflow discussion argues that the engineering bottleneck has moved toward deciding and checking correctness.
What should a coding-agent spec contain?
A useful spec should be short enough to stay current and precise enough to prevent predictable misunderstandings. For most bounded engineering tasks, include the following elements:
| Element | Purpose | Example |
|---|---|---|
| Goal | Defines the outcome | “Add filtering to the project list.” |
| Examples | Makes intent concrete | “Filtering ‘api’ returns API-related projects.” |
| Non-goals | Limits scope | “Do not change the database schema.” |
| Acceptance criteria | Defines completion | “Empty results show a useful empty state.” |
| Failure behavior | Prevents happy-path bias | “Invalid filters return a validation error.” |
| Validation commands | Makes correctness repeatable | “Run the unit tests and production build.” |
Why vague prompts create expensive rework
A prompt such as “build a project filter” appears efficient because implementation starts immediately. In practice, the missing decisions return as correction loops: the reviewer clarifies behavior, the agent changes the code, tests reveal another assumption, and the reviewer repeats the process. The code may be produced quickly, but the total path to a trustworthy change becomes longer.
The problem is not that an agent needs every implementation detail. The problem is that it needs the important product and engineering boundaries. It should be free to choose a reasonable implementation inside those boundaries, while the boundaries themselves should be explicit.
A practical five-step workflow
1. State the outcome and the non-goals
Describe what the user or system should be able to do after the change. Then state what the task does not include. Non-goals are especially useful when an agent could reasonably expand the task into migrations, redesigns, or unrelated cleanup.
2. Add examples and edge cases
Examples reduce ambiguity more effectively than abstract adjectives such as “robust” or “intuitive.” Include normal input, empty input, malformed input, permission boundaries, duplicate data, and partial failure when those cases are relevant.
3. Convert important expectations into checks
Ask the agent to write or update tests, but review whether the tests verify behavior that matters. A test that merely confirms a function returns a value is weaker than a test that checks authorization, error handling, or an externally visible result.
4. Ask for a spec review before implementation
Before the agent writes code, have it identify contradictions, hidden dependencies, untestable claims, missing failure modes, and open questions. This makes the agent a critic of the plan instead of only an accelerator for the first interpretation of the plan.
5. Review the diff against intent
Passing tests are necessary but not sufficient. Review the final diff for scope creep, security assumptions, maintainability, performance, and consistency with the surrounding code. Human review should focus on product intent and system-level consequences rather than rechecking every mechanical transformation.
When more specification is worth the effort
Use more structure when a task crosses service boundaries, changes permissions, handles money or sensitive data, modifies a public API, or will be handed from one agent to another. These tasks benefit from typed contracts, explicit invariants, test fixtures, and clear failure behavior because a small misunderstanding can spread across the system.
For a small local refactor, a goal, a few examples, and a validation command may be enough. The best spec is proportional to the blast radius and the cost of being wrong.
Quick checklist for AI-assisted coding
- Can another developer explain what “done” means from the task description?
- Are the important non-goals and constraints visible?
- Are normal, empty, invalid, and unauthorized cases covered?
- Which requirements can become automated tests?
- What still requires human judgment?
- Has the final diff been checked for scope creep and unsafe assumptions?
The takeaway
Agentic development does not eliminate specification; it makes weak specification more expensive because vague intent can become a plausible implementation very quickly. Give the agent structured intent, concrete examples, explicit boundaries, and executable checks. Then reserve human attention for the decisions that tests cannot fully encode.