Cortex
Assign a ticket, watch AI ship the feature.
Cortex turns a ticket on a board into a reviewed, tested pull request. A developer connects their repository through a small command-line runner, drags a task across a Kanban board, and a chain of specialized AI agents plans the work, writes the code, reviews it, runs QA, and prepares the deployment - all running locally on the developer's own machine.
What Cortex Does
A team of AI agents that takes work from plan to pull request - safely.
Five-Agent Pipeline
Planner, Coder, Reviewer, QA, and DevOps agents pass work down the line, each one checking the last before anything moves forward.
Live Kanban Board
Drag a ticket from backlog to done and watch commits, runner status, and agent progress update in real time for the whole team.
Scope Guard
Agents may only touch the files declared in a ticket and can never read secrets or env files - enforced before every commit.
Local Runner CLI
A lightweight runner connects any git repository and executes on the developer's own machine using their existing AI coding subscription.
Meeting Intelligence
Drop in a transcript and Cortex extracts action items as tickets, flags blockers, and records the decisions that were actually made.
Project Brain
Semantic search across the entire codebase and past decisions, answering questions with the exact file and line the answer came from.
The Challenge
AI coding assistants are great at a single prompt, but the real work of shipping a feature is spread across planning, implementation, review, testing, and deployment. Teams wanted that whole loop automated without handing a black box the keys to their codebase, their secrets, or their production environment.
Our Approach
We split the work across five focused agents, each with a clear job and a checkpoint before the next one starts. The runner executes locally so code never leaves the developer's machine, a scope guard keeps every agent inside the files a ticket allows, and a semantic Project Brain gives the agents real context from the codebase and past decisions instead of guesswork.
The Outcome
Developers move tickets across a board and get back reviewed, tested changes ready to merge, with a live view of exactly what each agent did and why. The routine engineering grind shrinks, guardrails keep the automation honest, and the team stays focused on the decisions that genuinely need a human.
Technologies Used
A monorepo platform pairing a real-time dashboard with a local agent runner.
Frequently Asked Questions About This Project
Common questions about Cortex, the autonomous AI engineering platform
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