What it does

Tuning Engines treats AI agents and physical robots as a managed workforce. Each worker is registered with an owner, issued credentials and scoped permissions, and its actions, costs and outcomes are traced in one place. The site frames the product around four areas: compliance, security, observability and intelligence management. There are two ways to connect. Sidecar mode captures traces and telemetry out of band with no change to your traffic, and it is the only mode offered for robots today. Proxy mode, for software agents, sits in the request path through an OpenAI-compatible endpoint, so policy checks, approvals, budget caps, model routing, failover and caching apply before a call runs. Reporting covers spend by team, task and worker, mapped to the outcomes the work produced, along with evidence packs aimed at EU AI Act, NIST AI RMF and SOC 2 reviews. The page lists compatibility with tools such as Claude Code, Codex, Aider and Cline, with MCP, and with LangGraph and Temporal. Runtime security and compliance for robots is marked as coming soon.

Capabilities

Per-agent identity, credentials and least-privilege tool permissions
Inline policy checks, approvals and per-request budget caps in proxy mode
Sidecar mode for traces and telemetry without touching traffic
Model routing, failover and caching through an OpenAI-compatible endpoint
Spend attribution by team, task and worker, tied to outcomes
Audit evidence exports for EU AI Act, NIST AI RMF and SOC 2

When to use it

You run several AI agents and need to control which tools and data each one can reach
You want visibility into agent cost and traces before you start enforcing policy
You need audit evidence for AI governance reviews