Your AI coding agents are only as precise as their inputs. You write a task description. The agent interprets it generously. The code that comes back is plausible, not correct — because the agent didn't have traceable acceptance criteria, explicit scope, or any awareness of how this feature touches your existing system. That's not an agent problem. That's a requirements problem.
Tekk.coach generates structured, codebase-grounded requirements before any code is written. The agent reads your repository, surfaces the architectural constraints you didn't know to ask about, and produces requirements with acceptance criteria per subtask — not narrative text, not formatted bullet points, but structured requirements your coding agents can actually execute against.
How Tekk.coach Generates Requirements
Requirements generated without codebase context are aspirational. They describe what you want in isolation, unaware of the constraints, patterns, and dependencies in the system you're building into. Tekk changes the input before it generates anything.
Before producing a single requirement, the agent reads your repository using semantic search, file search, regex lookup, directory browsing, and repository profiling (languages, frameworks, services, packages). It builds an understanding of your actual system. Then it asks 3-6 questions grounded in what it found — not template questions, but questions about the specific constraints and trade-offs in your codebase.
The requirements output that results includes:
- TL;DR: What we're building and why, in one paragraph
- Building: Explicit in-scope functional requirements
- Not Building: Explicit out-of-scope requirements — the discipline that prevents scope creep
- Subtasks: End-to-end behavioral slices ("user can now do X"), each with:
- Acceptance criteria — concrete, verifiable, testable
- File references — which files are touched
- Dependencies — what must complete first
- Time estimates
- Assumptions: With risk levels and consequences if assumptions prove wrong
- Validation scenarios: Concrete end-to-end test cases
This is requirements-grade output. Not a PRD narrative. Not a formatted list. Structured artifacts with traceability from requirement to subtask to acceptance criterion.
Everything streams into a BlockNote editor as an editable working document — not a chat message you copy somewhere. The requirements persist, survive the planning session, and are linked to your kanban task.
Key Benefits
Requirements grounded in your actual system. Tekk reads your codebase before generating a single requirement. The output references your real files, frameworks, ORM patterns, and architectural constraints. Requirements that don't match your system cause rework. Requirements grounded in your system prevent it.
Acceptance criteria built in — for every subtask. Every subtask includes concrete, verifiable pass/fail criteria. Not "user is authenticated" — "user receives a 200 response and session cookie is set, magic link expires after 15 minutes, failed attempt returns 401 with standardized error payload." Missing acceptance criteria is where test coverage gaps and implementation disputes come from. Tekk closes that gap automatically.
Explicit scope enforcement. The "Not Building" section is not optional text you fill in later. It's a required output of every planning session. What's out of scope is as important as what's in scope, and Tekk generates both.
Multi-turn planning, not one-shot generation. Good requirements come from informed questions. Tekk's workflow is Search → Questions → Options → Plan. The agent reads your code, asks questions about actual constraints it found, and optionally presents architectural options with tradeoffs. The requirements reflect your decisions, not the AI's assumptions.
Requirements your coding agents can execute. The output format — behavioral subtasks with acceptance criteria and file references — is designed for AI coding agents. Cursor, Codex, and Claude Code perform significantly better against precise, structured requirements than against task descriptions.
How It Works
Step 1: Connect your codebase. GitHub, GitLab, or Bitbucket. Tekk's agent indexes your repository with semantic search so every requirement references real code.
Step 2: Describe what you need. Plain language. "Add multi-tenant support to the user model." "Build a webhook ingestion pipeline." "Implement RBAC on the API layer." The agent takes it from there.
Step 3: Answer architecture-grounded questions. The agent asks 3-6 questions based on what it found in your code. These questions surface constraints you may not have considered — existing patterns that constrain implementation choices, dependencies that affect ordering, assumptions that need to be made explicit.
Step 4: Review architectural options (when applicable). When multiple valid approaches exist, the agent presents 2-3 options with honest tradeoffs. You decide the direction before requirements are locked.
Step 5: Review and edit your requirements. The complete requirements output streams into the editor in real-time. Every subtask has acceptance criteria. Scope is explicit. Dependencies are ordered. Edit anything before execution begins.
Who This Is For
Engineers and technical leads who need requirements that are precise enough to produce deterministic code from an AI agent. If your team's AI-generated code keeps diverging from intent, the requirements are the problem.
Technical PMs building with AI coding agents who need requirements grounded in system architecture, not just user stories. You've written enough "As a user, I want..." stories that got misimplemented because the engineer (or the agent) couldn't infer the system constraints.
Founders and solo builders who understand what requirements are and don't want to write them manually for every feature. Connect your repo, describe the feature, answer 5 questions, and get production-quality requirements in under 10 minutes.
Not for teams that want narrative PRDs for stakeholder alignment. Not for product work that doesn't touch code. Tekk generates technical requirements for engineering execution — that's its scope, intentionally.
What Is an AI Requirements Generator?
An AI requirements generator uses language models to produce structured software requirements from natural language input. Unlike PRD generators — which produce narrative documentation for stakeholders — requirements generators produce engineering artifacts: acceptance criteria, functional requirements, dependency ordering, and scope boundaries that can be traced from requirement to implementation to test.
The technical framing matters. "Requirements" implies traceability — each requirement maps to a subtask and a test case. It implies acceptance criteria — concrete, verifiable conditions that define when a requirement is satisfied. It implies scope management — explicit in/out-of-scope designation, not just a description of what's being built.
AI requirements generation became strategically important alongside the rise of AI coding agents. The quality ceiling for any coding agent is the quality of the requirements it receives. As developers hand more implementation work to Cursor, Codex, and Claude Code, the precision of requirements has become the primary variable in code quality. Tools that generate these requirements from codebase context — not just from natural language descriptions — produce measurably better agent outputs.
Ready to Try Tekk.coach?
Stop giving your coding agents vague inputs and hoping for precise outputs. Connect your repo, answer 5 questions, and get structured requirements with acceptance criteria that your agents can actually execute against.