AI Coding Agent
You've got a solid AI coding agent — Cursor, Claude Code, Codex. It's fast. It writes real code across real files. And it still keeps producing rework, scope creep, and implementations that technically run but aren't what you asked for.
The problem isn't the agent. It's what you gave it.
Tekk.coach is the intelligence layer above your coding agent. It reads your codebase, asks informed questions, generates a structured spec — then hands it to your agent. Your agent finally knows exactly what to build, what not to touch, and what done looks like.
How Tekk.coach works with AI coding agents
Cursor is good. Claude Code is good. Codex is good. The gap isn't the agent — it's the input.
Most developers hand their agent a sentence. "Add OAuth to my app." "Fix the checkout flow." The agent doesn't have your codebase in its head. It makes assumptions, builds from those assumptions, and you spend an hour untangling output you wouldn't have chosen. That's a spec problem, not an agent problem.
Tekk fixes the spec. Before generating anything, it reads your codebase via semantic search, file search, and directory browsing across GitHub, GitLab, or Bitbucket. It asks 3-6 questions grounded in what it actually found — not generic questions. Real ones: "You're using Passport.js for Google OAuth — should magic links share that session model or use a separate JWT flow?"
You answer. Tekk presents two or three architecturally distinct approaches with honest tradeoffs. You pick one. Tekk writes the full spec.
What an ai powered coding agent actually needs: what's being built, what's explicitly not being built, subtasks described as behavior rather than file names, acceptance criteria per subtask, file references, assumptions with risk levels, and validation scenarios. That spec streams into a working document in the editor. Not a chat message. A document your team works from — and edits before handing off.
The contrast is concrete. What you type into Cursor: "Add magic link auth." What Tekk hands to Cursor: database schema, API routes, acceptance criteria per subtask, file targets, dependencies, and a "Not Building" section — specific to your repo's actual language, framework, and ORM.
That's why the agent ships the right thing.
Key benefits
Codebase-grounded specs your agent can actually execute Tekk reads your repo before writing anything. Every subtask references actual files and patterns from your codebase. No generic boilerplate. No "I assumed you were using Express" surprises mid-implementation.
Explicit scope boundaries — no more runaway builds Every plan includes a "Not Building" section. Your coding ai agent knows exactly what to touch and what to leave alone. This directly addresses the most common frustration in agentic coding: changing one thing and watching three others break.
Structured subtasks with acceptance criteria "User can log in with a magic link" with acceptance criteria and file targets executes differently in an agent than "update the auth flow." The spec you feed in determines what you get back.
Works with any coding agent — Cursor, Claude Code, Codex, Gemini Tekk is agent-agnostic. The spec it produces is a structured document you hand to whatever agent you're already using. You choose the execution layer. Tekk gives it what it needs to perform.
How it works
Step 1: Connect your repo Link GitHub, GitLab, or Bitbucket. Tekk reads your codebase via semantic search, file search, and directory browsing. It understands your stack before you describe the problem.
Step 2: Describe the feature A sentence or a paragraph — either works. Tekk uses your codebase as the real context, not the words you used to describe the goal.
Step 3: Answer a few questions 3-6 questions grounded in what Tekk found in your code. If the code already answers a question, Tekk doesn't ask it.
Step 4: Review your options Two or three architecturally distinct approaches, each with honest tradeoffs. Pick one. If there's one obvious path, Tekk skips this and moves straight to the spec.
Step 5: Get the spec — hand it to your agent Tekk writes the full plan into the task editor in real time. Review it, edit if needed, then copy it to your ai agent coding workflow. Paste into Cursor. Hand it to Claude Code. Run it with Codex. Your agent has what it needs.
Execution dispatch — where Tekk sends the approved spec directly to your connected agent and tracks progress on the kanban board — is coming next.
Who this is for
You're already using a coding agent. You've shipped real things with it. You also know the results are inconsistent — sometimes the agent nails it, sometimes you spend more time reviewing and correcting than you would have spent writing the code yourself.
That inconsistency almost always traces back to the spec. Not the agent.
- Solo founders and indie builders who can't afford rework. Plan it right once and know it's going to work.
- Developers using Cursor, Claude Code, or Codex who get inconsistent results and want to understand why. The answer is almost always the prompt.
- Small teams (1-10 people) without a dedicated architect — Tekk fills the knowledge gaps that would otherwise take days to research or thousands to consult.
- Product managers who need technically grounded specs — real specs a coding agent can execute, not AI-summarized notes or blank templates.
Tekk is not for senior engineers who already write tight specs, or enterprise teams that need Jira-style process governance. It's for people who want to move fast and get it right the first time.
What is an AI coding agent?
An AI coding agent is an autonomous system built on a large language model that performs software engineering tasks without step-by-step human guidance. Unlike earlier AI assistants that responded to a single prompt and waited, a coding agent reads code, plans changes, writes across multiple files, runs tests, observes results, and iterates — often for minutes at a stretch without a human in the loop.
The category evolved fast. By 2026, roughly 85% of developers use AI tools regularly, and 65% use them at least weekly. The major players each bring something different to the table:
- Cursor — the most widely adopted IDE-based agent among individuals and small teams. Visual diffs, multi-file editing, Background Agents for longer runs.
- Claude Code — fastest-growing in the category. CLI-based, strong contextual reasoning, 46% "most loved" rating in early 2026 developer surveys.
- OpenAI Codex — repo-scale reasoning, deterministic multi-step execution, coordinates changes across the full codebase.
- GitHub Copilot — enterprise standard. Inline suggestions, agent mode added in 2025, lowest entry cost.
- Windsurf — ranked #1 for value-for-money (LogRocket, February 2026). Fully agentic Cascade feature.
Any ai agent for coding is only as good as the input it receives. The agents have gotten genuinely capable. The bottleneck has shifted — it's not the model, it's the spec. Tekk makes the spec precise.
Start planning free
Every coding agent you're paying for is capable of better output. The bottleneck is the spec.
Connect your repo, describe the feature, get a structured plan your agent can actually execute. No PRDs. No alignment meetings. Just specs that work.