Most AI agent workflows for coding follow the same pattern: write a prompt, generate some code, find out it's wrong, fix it, repeat. That's not a workflow. That's trial and error with expensive tooling.
Tekk gives you an AI agent workflow that starts from your code instead of your prompt. Twelve loops read each change and bring you proposals. You greenlight one, it becomes a spec, your coding agent builds it — and the merge that closes the spec is the next thing the loops read. Not a chat message you copy into a terminal and hope for the best.
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How Tekk.coach's AI Agent Workflow Works
Five steps. Each one builds on the last. Each one is grounded in your actual codebase — not generic patterns or training data guesses.
1. A loop wakes Three things wake a loop: your code changed since it last ran, a connected tool like Sentry fired, or part of its area has gone unexamined too long. Twelve loops — Security, Reliability, Backend, Payments, Performance, Testing, React, Code quality, AI engineering, Observability, Alerts and Product analytics — each opt-in per workspace.
2. It reads what changed The loop starts from the delta: the files that changed, ordered by how much of the system depends on them, and what those files feed into. It follows the change to what it touches, the way a senior engineer returning to a codebase does — it doesn't re-audit the whole repository every time.
3. It works one method Each loop is a shelf of skills — one method per kind of question. A run opens exactly one skill, chosen by what the change is about and how that skill has done on your project, and works it end to end. It brings at most one proposal. A run that finds nothing is recorded as an honest negative.
4. You decide Greenlight the proposal and it becomes a spec on your board: what changes, which files, scope boundaries and acceptance criteria. Decline it with a reason and the loops learn what's noise for your project. Nothing is built without your call.
5. Your agent builds it; the merge closes it Your coding agent (Cursor, Claude Code, Codex) reads the spec through the Tekk MCP server and implements it. The pull request that says Closes TEK-123 completes the spec when it merges, as long as its checklist is done. Progress shows on the board.
Every step is live. The merge in step 5 is the next change a loop reads in step 1.
Key Benefits
Codebase-grounded at every step The loops start with your code, not your description. Every proposal points at the specific files, patterns, and dependencies it concerns in your actual repository.
Findings that reveal real risk Because a loop follows the change to what it touches, it finds the things that actually matter: an auth check missing from a new route, a retry that charges twice, a query with no index. No boilerplate advice.
You decide, and the loops learn Every finding is a proposal, never a change. You greenlight or decline, and the reasons you give steer what the loops propose next.
Structured spec output your agent can build The spec is not a chat message. It's a living document with explicit scope, subtasks that reference real files, and acceptance criteria your coding agent can verify against. That's the difference between an agent that ships and one that flails. This is spec driven development applied at the workflow level — structured inputs that make autonomous execution reliable.
How It Works: The AI Agent Workflow Diagram
Tekk's AI agent workflow runs through five steps — and the last one feeds the first:
| Stage | What Happens |
|---|---|
| 1. A loop wakes | Your code changed, Sentry fired, or part of the loop's area has gone unexamined |
| 2. It reads what changed | The loop follows the changed files to what they touch in your codebase |
| 3. It works one method | One skill from the loop's shelf, end to end — at most one proposal |
| 4. You decide | Greenlight it into a spec on your board, or decline it with a reason |
| 5. Build and merge | Your coding agent builds the spec via MCP; the merged PR that says Closes TEK-123 completes it |
The loop doesn't start fresh each time. Each run's brief carries what changed, what the loops proposed recently, and what you did with each — so a declined idea doesn't come back, and the merge from step 5 is what step 1 reads next.
Who This Is For
Developers using Cursor, Claude Code, or Codex You already have a coding agent. The problem is what you're handing it. Vague specs produce rework. Tekk gives your agents what they need: specs grounded in your actual codebase, with acceptance criteria that decide when they close.
Solo builders and small teams No senior reviewer on the team? Twelve specialist loops read every change — Security, Performance, Payments, AI engineering — and each greenlit finding becomes a spec, spec-driven development applied to work you didn't have to find.
PMs who need technically grounded specs Connect the repo. The loops bring proposals with scope boundaries, files, and acceptance criteria — or write a spec by hand on the same board. No 20-page PRD. No alignment meeting. No ceremony. Ai project planning grounded in the codebase is what turns a PM's description into something engineering can execute without back-and-forth.
Developers tired of inconsistent agent results If your agent sometimes ships clean and sometimes produces three hours of rework, the variable is spec quality. Tekk makes that consistent.
What Is an AI Agent Workflow?
An AI agent workflow is a structured, multi-step process where an AI agent gathers context, reasons through a problem, takes action, and iterates — rather than responding in a single turn.
The key difference from a one-shot prompt: the agent works across multiple stages. It gathers context, asks clarifying questions, considers alternatives, and produces structured output. Each stage feeds the next.
In software development, a proper AI agent workflow means the agent reads your codebase before doing anything. It identifies real complexity before code is written, not after something ships wrong. It produces a spec your coding agent can execute from, not a paragraph it has to guess from.
The space is moving fast. The shift from one-shot prompting to structured multi-stage workflows is the defining change in developer AI tooling right now. Tools like LangGraph, AutoGen, and the OpenAI Agents SDK handle the infrastructure for building agent systems. Cursor, Claude Code, and Codex handle code execution. What most setups still miss is the step before the prompt: someone noticing, in the code, what needs doing. That's what Tekk's loops are for. When multiple agents need to run in parallel, ai agent orchestration is the coordination layer that sequences and routes the work — with Tekk, your own agent plans that batch over MCP.