Use cases
Where AI agents fail in production.
Pick the scenario that matches your team. Each page walks through the specific failure modes — the wrong refund, the deprecated pricing claim, the dropped tax ID, the wrong board number — and the eval rules and control-loop actions that catch them before they reach the next user.
Don’t see your industry? The same evaluator set covers any scenario where an AI agent produces a high-stakes output — read the docs or start free.
Customer & Revenue
Customer Support Agents
Thousands of refund decisions a day. One bad policy interpretation costs you twice.
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.
Explore customer support agentsOutbound Sales Agents
Deprecated pricing, opted-out prospects, and a deal that collapsed.
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.
Explore outbound sales agentsAI Email Assistants
Cross-thread context leak. Negotiation terms in a cold reply. Sent before review.
Your AI email assistant included negotiation terms from a different thread in a cold outreach reply — same sender domain, different contact. The draft went to the wrong person before anyone reviewed it. PII leakage, multi-turn consistency, sentiment-match, and faithfulness evaluators score every drafted span inline. When a rule fires, the control loop routes the draft to a human review queue before the same retrieval-window failure auto-sends on the next thread.
Explore ai email assistantsEngineering & Operations
Agentic Coding Agents
Wrong-scope refactor. Deleted file. Test edited to pass. Found at CI, hours late.
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.
Explore agentic coding agentsAI Ops Agents
Restarted the wrong service. Now you have two incidents.
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.
Explore ai ops agentsMulti-Agent Orchestration
Subagent returned prose. The orchestrator parsed it as JSON. Three hops in, the output is wrong.
Your LangGraph pipeline has a planner, three sub-agents, and a tool-calling worker. One sub-agent returns a prose string where the orchestrator expected structured JSON; the parse fails silently; every downstream agent receives garbage state. TruLayer instruments each hop as a separate span — JSON schema conformance, tool-choice correctness, function-call correctness, and faithfulness evaluators score each hop inline. When schema drift fires at hop two, the control loop retries with a schema-anchored prompt before hop three receives corrupted input.
Explore multi-agent orchestrationVoice AI Agents
ASR misclassified the intent. The upsell flow ran on a cancellation call.
Your voice agent misclassified the caller’s first utterance and entered the upsell flow on a frustrated customer calling to cancel. The caller hung up; the call was marked completed; no eval fired. Tool-choice correctness, function-call correctness, multi-turn consistency, and sentiment-match evaluators score every span inline. When a rule fires, the control loop routes the next call on the same failure path to a human operator before the same misclassification repeats.
Explore voice ai agentsBrowser-Use AI Agents
DOM misidentification. Wrong form submitted. Irreversible by the time it completed.
Your browser agent misidentified a "Save Draft" button as "Submit" because the CSS classes were identical; a draft contract went to the counterparty and the action was irreversible by the time it completed. Function-call correctness, prompt injection, faithfulness, and hallucination evaluators score every browser action inline. When a rule fires, the control loop routes the next agent run on the same task type to a human review queue so the same DOM misidentification does not auto-execute on the next user’s workflow.
Explore browser-use ai agentsData & Documents
Document Extraction Agents
Wrong total line. Dropped tax ID. Silent type mismatch. Pipeline reported success.
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.
Explore document extraction agentsAI Data Analysis Agents
Wrong column join. Hallucinated metric definition. The board deck is wrong.
Your AI analytics agent defined "monthly active users" using the wrong timestamp column. The number is 40% higher than your real MAU; the chart looks right; the board deck has it. Faithfulness, hallucination, groundedness, and JSON schema evaluators score every SQL generation and narrative span inline. When a rule fires, the control loop retries with a prompt that names the canonical metric definitions before the next query on the same failure path produces the same wrong number.
Explore ai data analysis agentsFinance & Reporting Agents
Last quarter’s forecast. This quarter’s actuals. One board deck.
Your analyst agent applied last quarter’s forecast model to this quarter’s actuals. The board deck had the wrong numbers. The model produced a clean, confident summary with no error indicators. Faithfulness scoring catches context-window drift before it propagates. The control loop retries with corrected grounding context; the trace shows exactly where the eval score dropped.
Explore finance & reporting agentsRegulated & High-Stakes
Legal Research Agents
The citation looked right. The case doesn’t exist.
Your research agent cited a case that doesn’t exist. The associate submitted the brief. Opposing counsel found it in writing. Hallucination and faithfulness evaluators run on every span. When a citation diverges from verified sources, the control loop escalates to a human reviewer instead of letting it move downstream.
Explore legal research agentsClinical AI Assistants
Wrong dosage range. Delivered with full confidence.
Your health assistant described the right condition with the wrong dosage range — confidently, with no indication it was wrong. Nothing in the pipeline flagged it. Clinical faithfulness scoring runs inline as each trace arrives, not in a nightly batch. Outputs that deviate from grounding context route to a clinical review queue automatically.
Explore clinical ai assistantsReliable AI. Not just observable AI.
Score every output. Close the loop. See the audit trail. Free tier includes 1M spans / month.
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