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. VentureBeat reports the core issues: brittle context windows, butterfly-effect changes, and silent failures.

The problem isn't the agent. It's what you gave it.

Tekk.coach gives your coding agent work worth doing, written properly. Twelve loops read your codebase and bring you proposals; greenlight one and it becomes a spec your agent reads through the Tekk MCP server. Your agent finally knows exactly what to build, what not to touch, and what done looks like.

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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. As The Gnar Company explains, LLMs are biased toward forcing solutions rather than asking for missing context. That's a spec problem, not an agent problem.

Tekk fixes the spec — and who has to write it. Its loops start from what changed in your code, follow it to what it touches, and work one method end to end, so a finding is about your code, not generic advice. Real ones: the new route takes the account id from the request instead of the session, so any signed-in user can read another tenant's invoices. This is spec driven development applied to work nobody had to notice — producing specs your agent can actually execute instead of interpret.

You read the proposal. Greenlight it and it becomes a spec on your board. Decline it with a reason and the loops learn what's noise for your project.

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 lives on your board as a working document. 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 a Tekk spec 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's loops read your repo before proposing anything. Every subtask references actual files and patterns from your codebase. No generic boilerplate. No "I assumed you were using Express" surprises mid-implementation. Simon Willison found that agents work best on well-defined tasks with clear acceptance criteria — exactly what Tekk produces.

Explicit scope boundaries — no more runaway builds Every spec includes a "Not Building" section. Your coding ai agent knows exactly what to touch and what to leave alone. Addy Osmani's AI coding workflow confirms it: break work into small, well-scoped chunks with clear boundaries. 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 MCP-capable agent — Claude Code, Codex, Cursor Tekk is agent-agnostic. Claude Code, Codex, Cursor and any other MCP-capable client read the spec through the Tekk MCP server. You choose who builds. Tekk gives it what it needs to perform.

How it works

Step 1: Connect your repo Connect GitHub and turn on the loops you want. In Claude Code, one plugin install connects the Tekk MCP server; Codex and Cursor connect to the same server.

Step 2: A loop finds the work When your code changes, Sentry fires, or part of an area has gone unexamined too long, the loop that owns it wakes. It reads what changed and works one method from its shelf of skills.

Step 3: Greenlight or decline At most one proposal per run, tied to your actual files — or an honest "nothing found". Greenlight it, or decline it with a reason the loops learn from.

Step 4: Your agent builds the spec A greenlit proposal becomes a spec on your board. Your agent lists specs, reads one in full, marks it in progress, and implements it — all over MCP. You can still write a spec by hand; it lands on the same board.

Step 5: The merge closes it Open the PR from the spec's branch, or write Closes TEK-123 in it. When it merges and the checklist is done, the spec completes on its own — and that merge is the next change the loops read.

Over MCP your agent can also plan a collision-free batch of specs and run them as parallel sessions, each merged PR closing its spec. For teams coordinating multiple agents across complex workflows, ai agent orchestration provides the coordination layer above the individual coding agent.

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's specialist loops — Security, Payments, Reliability — catch what a specialist would, on every change. Specs grounded in your codebase are what separate teams that ship correctly from teams that rework repeatedly.
  • 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. According to the Stack Overflow 2025 Developer Survey, 84% of developers now use AI tools — but only 29% trust the outputs to be accurate. Gartner projects that 90% of software engineers will use AI code assistants by 2028. A Morph LLM study testing 15 agents found that agent scaffolding matters as much as the model — the same model scored 17 problems apart depending on which agent wrapper it ran inside. 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's loops find the work and make the spec precise.