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.
We design agent development cycles that can work continuously without routing every implementation decision through a person. Engineers validate architecture, define constraints and evals, and evolve the internal harnesses; agents execute within those boundaries around the clock. The public machine interfaces on relux.works and api.relux.works use the same approach in production.
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 automatic escalation tied to eval results
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 keeps routine implementation within measurable boundaries and escalates only when evals fail. Engineers can spend their time on architecture and harness evolution.
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 starts with tests around critical behavior, eval gates in CI, and audit trails. Agents get more freedom when the metrics pass; engineers validate the architecture and adjust the harness when results expose a gap.
What proof do you have that this works?
The public interfaces are directly checkable: an agent can discover relux.works, read structured service and pricing data, and submit an inquiry through api.relux.works/mcp after explicit user approval.
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.
https://api.relux.works/mcpPrefer the old-school route?: ivan@relux.works