TruLayer — Evals, Closed Control Loop & Auto-Rollback for Production AI
Control Loop v0.1 — now live<br>Your AI nails the demo. TruLayer makes it nail production.<br>Evals score every output. A closed control loop retries with a fallback model, gates high-stakes actions on human approval, and rolls back automatically when a fix introduces a new regression — turning every production failure into a system fix before it hits the next user.<br>Start free Read the docs<br>Free tier includes 1M spans / month · No credit card
Ingestion latency<br>1MFree spans / month<br>SOC 2Type II in progress<br>99.9%Uptime target
Works with<br>OpenAIAnthropicClaude (MCP)LangChainLangGraphAutoGenCrewAILlamaIndexPydanticAIDSPyHaystackVercel AI SDKMastraLlamaIndex-TSCustom LLMs
How teams use TruLayer<br>Observe. Evaluate. Improve.<br>Most tools stop at the trace. TruLayer takes you from “something’s wrong” to “here’s the fix, shipped” in one platform.
Observe<br>See what’s happening. Understand why.<br>Distributed traces, failure clustering, anomaly detection, and semantic search — everything you need to go from "something’s wrong" to "here’s exactly why." Configurable retry depth prevents runaway cascades: when a trace has been retried N times without passing eval, it escalates to the human-in-the-loop queue automatically.<br>Explore observe<br>Evaluate<br>Know whether it was correct, not just whether it ran.<br>25 pre-built evaluators, eval rules on any span, regression testing against golden datasets, and score trends over time.<br>Explore evaluate<br>Improve<br>Close the loop before the next user hits it.<br>AI-suggested prompt improvements, self-healing actions, human-in-the-loop approval, and remediation diffs — the full control loop.<br>Explore improve
Who uses TruLayer<br>AI agents handle millions of decisions. Here’s where they go wrong.<br>TruLayer keeps them on track — automatically, at the system level, before the same failure hits the next user.
Customer & Revenue
Customer Support Agents<br>Thousands of refund decisions a day. One bad policy interpretation costs you twice.<br>Your refund agent handles thousands of decisions a day. One bad policy interpretation issues $1,000 instead of $500, another invents a department that doesn’t exist — and you find out from a support ticket, not a trace. Eval rules score every refund decision inline. When a rule fires, the control loop retries with a corrected prompt or routes the case to a human queue — the next customer gets the right answer.<br>Explore customer support agents<br>Outbound Sales Agents<br>Deprecated pricing, opted-out prospects, and a deal that collapsed.<br>Your SDR agent quoted a pricing tier you deprecated six months ago, then emailed a prospect who had opted out last quarter. The deal collapsed; legal is now involved. Faithfulness scoring flags outputs that drift from your pricing and compliance context. The same failure class doesn’t reach the send queue twice.<br>Explore outbound sales agents
Engineering & Operations
Agentic Coding Agents<br>Wrong-scope refactor. Deleted file. Test edited to pass. Found at CI, hours late.<br>Your coding agent refactored a module that a parallel branch already rewrote, deleted a file based on a truncated context window, and edited a test to make it pass. CI catches the deletion hours after the agent session closed; the rest only surfaces in staging. Function-call correctness, prompt injection, and faithfulness evaluators score every tool call inline. When a rule fires, the control loop retries with a corrected file scope or routes the next agent run on the same failure path to a human review queue.<br>Explore agentic coding agents<br>AI Ops Agents<br>Restarted the wrong service. Now you have two incidents.<br>Your incident response agent restarted a healthy service that shared a label with the degraded one. Now you have two incidents and it’s 2am. Tool-call correctness evaluators score every automated action inline. When a rule fires, the loop routes to a human before the next runbook step executes — not after the postmortem.<br>Explore ai ops agents
Data & Documents
Document Extraction Agents<br>Wrong total line. Dropped tax ID. Silent type mismatch. Pipeline reported success.<br>Your invoice extraction agent read the subtotal line instead of total-due and wrote the wrong amount to your ERP. A second invoice dropped a vendor tax ID because the field label varied from the template — the PII landed in the CRM anyway. A third had its amount field silently coerced to string; the type validation failed, the pipeline reported success, and nobody found out until reconciliation. PII leakage and tool-call correctness evaluators run inline on every span. When an extracted field is missing, malformed, or the wrong type, the control loop retries with a targeted prompt, falls back to a stricter extraction template, or routes to a human review queue — before the record propagates downstream.<br>Explore document extraction agents<br>Finance & Reporting Agents<br>Last quarter’s forecast. This quarter’s actuals. One board deck.<br>Your analyst agent...