Multi-Agent Coding Platform
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.
Tekk is building the multi-agent coding platform that fixes this from the foundation: plan first, then orchestrate. The planning layer is live today. The dispatch layer — running Cursor, Codex, Claude Code, and Gemini in parallel waves — is coming next.
How Tekk.coach Does Multi-Agent Coding
Part 1: Live Today — Spec Generation Built for Parallel Execution
Before any agent touches code, Tekk reads your codebase. It searches your repository using semantic search and file analysis, then asks targeted questions based on what it actually found — not generic boilerplate questions. From there it generates a structured spec with subtasks decomposed by dependency order.
Each subtask includes acceptance criteria, file references, a behavioral scope statement ("user can now do X"), and explicit dependencies on other subtasks. That dependency map is what enables parallel execution to work. Independent subtasks 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.
Every plan also includes an explicit "Not Building" section. Scope boundaries are defined before a single agent starts. This isn't ceremony — it's what prevents parallel agents from building the same thing three different ways.
The planning workflow (codebase read → questions → options → plan) is live now. You can use the structured specs with any coding agent today, manually.
Part 2: Coming Next — Dispatch and Orchestration Layer
Tekk will connect to Cursor, Codex, Claude Code, and Gemini via OAuth — the same way GitHub connects today. Once you approve a plan, Tekk takes it from there.
It groups independent subtasks into parallel execution waves and batches related subtasks — those that touch the same files or share context — into coherent jobs per agent. All jobs push to a single shared feature branch. You review one PR at the end, not one per agent.
Real-time progress appears on your kanban board as agents work. You can see which agents are running which jobs and what's completed.
Key Benefits
Spec decomposition built for parallel execution (live) Every Tekk plan breaks subtasks into dependency order. Independent subtasks are explicitly identified so they can run simultaneously without stepping on each other. This is the structural foundation multi-agent execution needs to work correctly.
Dependency-aware subtask grouping (live) Tekk doesn't just list tasks — it maps their relationships. Which subtasks must complete before others can start. Which can run in parallel. You see this before any agent runs, and you can edit it.
Works with agents you already pay for (no new lock-in) Tekk isn't a coding agent. It orchestrates the ones you're already using — Cursor, Codex, Claude Code, Gemini. You don't switch tools. You add a coordination layer on top of what you already have.
Single PR output (coming next) All parallel jobs push to one shared feature branch. One PR. One review. The merge point is where multi-agent chaos usually surfaces — conflicting implementations, divergent patterns, review overhead that wipes out the time you saved. Tekk handles the merge coordination so you don't have to.
How It Works
Step 1: Connect your repository Tekk supports GitHub, GitLab, and Bitbucket. It reads your actual codebase — languages, frameworks, file structure, existing patterns — before generating anything. No pasting context manually.
Step 2: Describe what you're building Tell Tekk what the feature is. It asks 3–6 questions grounded in what it found in your code. Not generic questions. Questions like "your auth layer uses magic link — should this feature reuse that flow or add a separate session type?"
Step 3: Get a structured spec with dependency-ordered subtasks (live) The plan streams into your task editor as a living document: TL;DR, Building / Not Building scope boundaries, subtasks with acceptance criteria and file references, dependencies, assumptions with risk levels, and validation scenarios. Edit it before anything executes.
Step 4: Approve and dispatch (coming next) Click Execute, select your agents, confirm. Tekk decomposes the approved subtasks into parallel waves based on dependencies. Independent groups run simultaneously across Cursor, Codex, Claude Code, or Gemini — each agent working in isolation on its assigned jobs.
Step 5: Review one PR (coming next) All jobs push to a shared feature branch. When agents complete, you get notified. Review the PR on GitHub. Merge when you're satisfied. The kanban card marks the task complete.
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. Tekk gives you the planning foundation those agents 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 subtask structure makes 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. Tekk solves that problem, and the orchestration layer will let you run multiple agents simultaneously 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. 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?" 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 planning quality as the prerequisite for orchestration: structured specs and dependency-ordered subtasks 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.
Start Planning Free →
Parallel agents executing a bad spec just fail faster. Tekk ensures the spec is right before any agent touches code — codebase-grounded, dependency-ordered, scope-bounded. The planning layer is live now. The dispatch layer is coming.
Connect your repo and generate your first spec in a few minutes.