Running multiple AI coding agents on the same codebase without a plan is chaos. Conflicting changes. Duplicated work. Three PRs that can't be merged. The problem isn't the agents — it's that they all started from the same underspecified instruction. As Mike Mason argues in Coherence Through Orchestration, Not Autonomy, the missing layer in AI coding isn't better models — it's better coordination.
Tekk fixes this from the foundation: a board of specs that loops fill and your agents work. Loops read what changed in your code and propose specs you greenlight. Your coding agents — Claude Code, Codex, Cursor — run them in parallel through the Tekk MCP server. A merged PR closes each one. All of it is live today.
How Tekk.coach Does Multi-Agent Coding
Part 1: Loops Fill the Board
Before any agent touches code, the work has to exist as a spec. Tekk's twelve loops find it: when your code changes, when Sentry fires, or when part of an area goes unexamined, a loop wakes, reads what changed, and brings at most one proposal. You greenlight it into a spec or decline it with a reason, and the loops learn what's noise for your project.
Each spec carries acceptance criteria, a build-order checklist, the code areas it touches, and the specs it dependsOn. That map is what enables parallel execution to work. Independent specs can run simultaneously. Dependent ones queue in order. Without this structure, parallel agents don't know what to do in what order — and they step on each other.
A feature that decomposes becomes a parent spec with one sub-task per independently buildable part, each with its own PR. Scope is set before a single agent starts. This isn't ceremony — it's what prevents parallel agents from building the same thing three different ways. This is spec driven development as the prerequisite for multi-agent execution: the plan comes first, then the agents run.
Loops can't see everything you want built. When you already know the feature, write the spec yourself — it lands on the same board and runs the same way.
Part 2: Your Agents Work the Board in Parallel
Your coding agent connects to the board through the Tekk MCP server — one plugin install in Claude Code, a workspace URL for Codex, Cursor, and other MCP clients. Tekk never writes code; your agents do.
plan_batch picks the next collision-free set: it refuses to co-schedule two specs whose code areas overlap and holds back any spec whose dependency isn't finished. The blitz skill then opens a coding session per spec — as many at once as is collision-safe — and launches nothing without your yes.
fleet_status answers "what are my agents doing, and what's left to launch?" Completion is derived, never claimed: a session is done when its spec's closing PR merges.
Key Benefits
A board that fills itself (live) Loops read every change and propose the work as specs. Your agents don't run out of well-scoped work, and you decide every item that reaches them.
Dependency-aware batches (live)
Specs declare what they depend on and which code they touch. plan_batch respects both, so agents in the same wave don't collide in the same files. You see the batch before any agent runs.
Works with agents you already pay for (no new lock-in) Tekk isn't a coding agent and never writes code. Claude Code, Codex, and Cursor work the board through MCP. You don't switch tools. You add a board, and the loops that fill it, on top of what you already have. That covers the planning side of ai agent orchestration — dependency mapping and collision-free batches — without a separate tool.
Merges close the work (live)
Each spec closes from its own PR: Closes TEK-123 completes it on merge once its checklist is done, and a parent completes when its sub-tasks do. The merge point is where multi-agent chaos usually surfaces — conflicting implementations, divergent patterns, review overhead that wipes out the time you saved. Google's 2025 DORA Report found that higher AI adoption correlated with a 154% increase in PR size and 91% more code review time — exactly the downstream cost that uncoordinated parallel agents create. Small specs with their own PRs keep each review small.
How It Works
Step 1: Connect your repository Connect your repo and turn on the loops you want. They read your actual codebase — every change, followed to what it touches. No pasting context manually.
Step 2: Greenlight what the loops find A loop brings one proposal at a time, pointing at the code it concerns — say, a route with no ownership check, or a retry that charges twice. Greenlight it into a spec or decline it with a reason. Have a feature in mind? Write the spec by hand.
Step 3: Specs with dependencies (live) Each spec has acceptance criteria, a build-order checklist, the code it touches, and the specs it depends on. Split a feature into sub-tasks so independent parts can run at once. Edit it before anything executes.
Step 4: Run a batch (live)
From Claude Code, Codex, or Cursor, plan_batch picks a collision-free set and blitz opens a coding session per spec. Independent specs run simultaneously, each agent working on its own spec.
Step 5: Merge closes the spec (live)
Each PR that says Closes TEK-123 completes its spec when it merges and the checklist is done. Review the PR on GitHub. Merge when you're satisfied. That merge is the next change the loops read.
Who This Is For
Teams running multiple agents and hitting coordination problems. You've tried spawning parallel agents. Some of it works, but you're spending time managing conflicts, re-explaining context to each agent, and sorting out what to merge. The chaos is eating the time savings. Deloitte's analysis of AI agent orchestration confirms that agent sprawl without a coordination layer destroys the efficiency gains. Tekk gives those agents the board they were missing.
Developers who want to parallelize AI coding output. You're using Cursor or Claude Code and building features sequentially. You want to run multiple agents in parallel but haven't had a reliable way to decompose the work without things colliding. Tekk's dependency-aware specs make parallelism intentional rather than accidental.
Solo founders and small teams who want to scale what they can ship. You're building with AI agents already. The bottleneck isn't the agents — it's getting a spec good enough that agents can execute without coming back with questions or going in the wrong direction. Structured ai project planning is what makes the difference between agents that flounder and agents that ship. Tekk's loops write those specs from your code, and the MCP server lets you run several agents at once without adding coordination overhead.
What Is a Multi-Agent Coding Platform?
A multi-agent coding platform coordinates multiple AI coding agents — each working on different parts of a codebase simultaneously — to compress development time through parallelism. In the original "conductor" model, a developer guides one agent at a time. In the orchestrator model, a platform decomposes a task into subtasks, assigns agents to each, manages workspace isolation so they don't conflict, and merges results into a single output.
The category emerged clearly in 2025–2026 as individual coding agents became capable enough to work autonomously on multi-file changes — a shift Anthropic documented in their 2026 Agentic Coding Trends Report, which found that multi-agent systems are replacing single-agent workflows as developers integrate AI into 60% of their work. The challenge shifted from "can the agent code?" to "how do you coordinate multiple agents working on the same codebase without them producing conflicting, redundant, or incoherent output?" Research backs this up: the MAGIS framework (NeurIPS 2024) showed that a multi-agent system with specialized roles — Manager, Developer, QA Engineer — achieved an eight-fold improvement over single-agent GPT-4 on GitHub issue resolution. That coordination problem is what multi-agent platforms address.
The landscape currently splits into two approaches. The first — tools like Conductor, Cursor 2.0 subagents, and OpenAI Codex multi-agent — focuses on execution coordination: spawn agents, isolate workspaces, provide dashboards, merge results. The second approach, which Tekk represents, focuses on where the work comes from: loops that propose specs from your code, with the dependencies and code areas that make parallel execution coherent rather than chaotic. Both are real approaches. They're not mutually exclusive — they're sequential. The plan has to come before the dispatch.