Start with the context.
Give the work a clear brief, grounded in your requirements, repositories and existing decisions.
An AI engineering lead
AI agents write the code.
Your team signs off on what ships.
Forelead runs coding agents in sandboxes, has a second model review every change, and brings the important decisions to the person who owns them. Nothing merges without a person's approval. Built for compliance-heavy teams, where every change has to be explained later.
In development. Taking on a few design partners.
Release workspaceExampleTeam project
Roles & permissions
A shared brief. A visible path forward.
Give the work a shared brief.
Bring requirements, project context and acceptance criteria together before the agents begin.
- Context
- Existing roles & access rules
- Goal
- Let team owners manage permissions
- Evidence
- Acceptance criteria & test plan
Keep the agents moving together.
Agents work in sandboxes with no access to your credentials. Each change goes through a separate reviewer before a person sees it.
- Tasks
- Access API, settings UI, tests
- Review
- A different model checks the change
- Policy
- Pause when a decision needs a person
The important call comes to you.
The question arrives with the options, a recommendation and the evidence, so you can answer it in one sitting.
- Question
- Who can grant administrator access?
- Options
- Team owners or designated admins
- Owner
- Your team decides, and the answer is recorded
Keep the evidence with the work.
Nothing merges until a person approves it. The requirement, decision, change, checks and approval stay linked for whoever asks later.
- Change
- Reviewed implementation
- Tests
- Results tied to acceptance criteria
- Record
- Requirement, decision & sign-off
Agents in sandboxes.
A second model reviews every change.
A person approves every merge.
From an idea
to an approved change.
Agents do the planning, coding and first review. Your team makes the calls that matter.
Coordinate the work.
Agents build in isolated sandboxes, and a second model reviews every change. You set what they can do on their own.
Make the call. Keep the record.
Review the important choices with their evidence, then approve the change with its decisions and check results attached.
Agents do the work.
People own the decisions.
For engineering teams that want to use AI agents without losing sight of what is being built, why it changed and who approved it.
Built for compliance-heavy software, where every change needs evidence an auditor or reviewer will accept.
Agents in sandboxes.
Each task runs in an isolated sandbox with limited network access. Agents never hold your credentials, and only the platform publishes their work.
Independent review.
A different model reviews every change, and checks run in the platform rather than on the agent's word.
A person on every merge.
Open questions go to their owner with options and a recommendation. You decide what agents can move forward, and nothing merges without a person's approval.
Traceable by default.
Each change stays linked to its issue, review, check results and approval.
Building next with design partners: signed decisions, exportable audit trails, and support for more agents, GitLab, Jira and Linear.
What would a good
AI engineering lead do for you?
We're working with a few teams on their real delivery workflow. Tell us where reviews, approvals or release evidence slow you down.
Kris, founder
Background in regulated software and in running AI agents on real engineering work. Forelead is built with the same agent workflow it sells.