Agent Harnesses and Teams That Ship With Them
The gap between a chat window and an engineering force is a harness: tools, guardrails, evals, and a trained team.
Most companies have tried AI coding assistants; few have turned them into a reliable engineering force. The difference is the harness: tool access, guardrails, evals, CI gates, and working protocols that make agent output trustworthy. We build that layer and train your team to run it. Our proof is public: relux.works scores a perfect 100 on Cloudflare's agent-readiness scanner, and an AI agent can discover, read, and hire our studio with no human on our side.
What we take on
Agent harnesses and scaffolding
The infrastructure that turns model calls into dependable work.
- Tool integrations, structured workflows, and guardrails for your domain
- Eval suites that measure agent output against your quality bar
- CI gates and review protocols so autonomy stays safe
Team training in agentic development
Your engineers, working the way we work.
- Hands-on programs for Claude Code, MCP, and skills-based workflows
- Pairing on your real codebase and tasks, not toy exercises
- Playbooks your team keeps and extends after we leave
Agentic rails for your codebase
Make any repository a place where agents work safely.
- Test coverage around critical behavior before autonomy is allowed
- Conventions, docs, and task tracking that agents can follow
- Progressive autonomy levels tied to eval results
Internal MCP servers and agent gateways
Expose your systems to agents on your own terms.
- MCP servers over your internal APIs, data, and tools
- Agent-facing gateways with OAuth, rate limits, and audit trails
- The pattern we run ourselves: api.relux.works is the reference
Frequently asked questions
What exactly is an agent harness?
Everything around the model that makes its work dependable: tool access, structured workflows, guardrails, evals, CI gates, and observability. A model without a harness is a demo; with one it is an engineering force.
How is this different from just giving developers AI assistants?
Assistants without structure produce inconsistent results and quiet failures. A harness plus working protocols gives you measurable quality, safe autonomy, and output your reviewers can trust. That difference is the whole engagement.
How long does it take to train a team?
A typical enablement runs two to four weeks: an intensive start, then pairing on your real tasks. Engineers ship agent-assisted work from the first week; the playbooks stay with you.
Which stacks do you work with?
Claude Code and the MCP ecosystem first, Cursor and custom harnesses where they fit. The principles (evals, gates, progressive autonomy) transfer across tools.
How do you keep agent autonomy safe?
Autonomy is earned, not assumed: tests around critical behavior first, eval gates in CI, review protocols, and audit trails. Agents get more freedom only where the numbers show they deserve it.
What proof do you have that this works?
Public and checkable: our own site went from 29 to a perfect 100 on Cloudflare's agent-readiness scanner in two days, driven by an agentic workflow. An AI agent can discover relux.works, read our pricing, and submit a real inquiry end to end at api.relux.works/mcp.
Can you build internal MCP servers over our systems?
Yes. We wrap your internal APIs, databases, and tools in MCP servers with proper auth, rate limits, and audit, so any MCP-capable assistant your team uses can work with your systems safely.
How is this priced?
Training programs and harness builds are quoted as fixed-scope engagements after a short discovery call. Ongoing evolution of the harness runs on a retainer, the same model as the rest of our services.
Make agents a dependable part of your team
Tell us about your team and stack: we reply within one business day with a proposed enablement shape and a fixed quote.
Email ivan@relux.works