How to Run Multi-Agent Dev Workflows with TAKT

TAKT promises "stop babysitting AI coding agents." In this hands-on walkthrough, we put that claim to the test: install the CLI, define a workflow in YAML, queue a task, and watch a multi-agent plan → implement → review → fix loop run with human checkpoints.

1. Install and configure

From any Git repository with at least one commit:

npm install -g takt

# First run: configure a provider in ~/.takt/config.yaml
# or use API key environment variables

SDK-based providers (claude-sdk, codex, opencode, pi) run on Node.js alone. CLI-based providers (Cursor, GitHub Copilot CLI, Kiro) require their external CLIs, and deepseek-harness additionally needs Python 3.10+.

2. Define the workflow in YAML

The heart of TAKT is a YAML workflow file. Each phase — planning, implementation, review, fix — gets its own persona, policy, knowledge, and output contract. Reviews cannot be silently skipped: findings route work back to fix steps, and human judgment is requested when needed.

3. Queue and run tasks

# Talk to AI, describe a task, use /go, then choose "Queue as task"
takt

# Execute queued tasks in isolated worktrees
takt run

# Review diffs, merge, retry, requeue, or delete task branches
takt list

Tasks run in isolated worktrees by default, and every step leaves logs and reports — so the path from task to pull request stays traceable and auditable. The same process is reusable, reviewable, and versionable across projects.

4. Beyond coding

TAKT is built primarily for AI coding workflows, but the model generalizes: any task where multiple agents must coordinate — or where review, judgment, and feedback loops improve quality — fits the topology. Governance, permissions, and human checkpoints are first-class citizens, not afterthoughts.