LEM. 1.1distortion under load

LEM. 1.2state under tilt

LEM. 1.3pattern from rotation

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Production monitoring for AI agents. Surface silent failures, pull context across traces, improve your agent before users churn.

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LEM. 2.1the search space

LEM. 2.2where agents wander

LEM. 2.3emergence from rules

ISSUES

Surface failures you didn't know to look for.

Lemma audits every trace against your agent's instructions and groups recurring failures into issues.

LIVE ALERTS

Get notified on what matters.

Lemma triages issues and alerts you in Slack.

LEMMA MCP

Fix it where you work.

Pull context into your coding agent to resolve it without context-switching.

METRICS

Ship with confidence.

After deploying the fix, Lemma creates an online eval. If a regression occurs, you'll know immediately.

#lemma-agent

LemmaAPP9:42 AM

🚨 New issue found in support-agent 🚨
When a customer can't be identified, the agent invents placeholder identifiers instead of asking — then files tickets and escalations against them. 33 of the last 50 runs affected.

Cole Gawin9:44 AM

@Lemma can you show me the traces?

LemmaAPP9:44 AM

Here are three representative traces, one per failure mode:

3 repliesToday at 9:44 AM

Cole Gawin9:45 AM

@Lemma how bad is this? :(

LemmaAPP9:45 AM

66% of support runs —
• 23 fabricated lookups
• 15 orphaned tickets with customerId: "unknown"
• 7 escalations routed to nonexistent tickets
Every one returned success, so nothing surfaced in error monitoring.
Suggested fix: require the agent to ask for the customer's email before any lookup — draft prompt change ready for review.

View in Lemma

📁 your coding agent

your coding agent

›fix the P0 refund-policy incident from Lemma

●lemma · get_incident(inc_4f2a)

└P0 — promising refunds outside 30-day policy

●lemma · get_traces(refund_policy)

└12 traces · root cause: prompt revision 41m ago

●Update(prompts/refund_policy.md)

└- Offer the customer a refund if they are unhappy.

+ Only offer refunds within the 30-day policy window.

+ Outside the window, route to a human agent.

✓ Fix applied — PR #482 opened

… +6 lines (ctrl+o to expand)

❋recap: Fixed the P0 refund-policy incident from Lemma: tightened prompts/refund_policy.md, replayed all 12 affected traces clean. Next action: merge PR #482 and watch the online eval.

›

›fix the P0 refund-policy incident from Lemma

●lemma · get_incident(inc_4f2a)

└P0 — promising refunds outside 30-day policy

●lemma · get_traces(refund_policy)

└12 traces · root cause: prompt revision 41m ago

●Update(prompts/refund_policy.md)

└- Offer the customer a refund if they are unhappy.

+ Only offer refunds within the 30-day policy window.

+ Outside the window, route to a human agent.

✓ Fix applied — PR #482 opened

… +6 lines (ctrl+o to expand)

❋recap: Fixed the P0 refund-policy incident from Lemma: tightened prompts/refund_policy.md, replayed all 12 affected traces clean. Next action: merge PR #482 and watch the online eval.

›

▶▶ bypass permissions on · 1 shell · ← for agents · ↓ to manage

INTEGRATIONS

Fits into your existing stack.

Native support for the frameworks your team already uses.

Generate API key

.env.example

LEMMA_API_KEY=your_api_key_here

LEMMA_PROJECT_ID=3a18f6d4b2907e

Paste into your coding agent

Prompt

Install the Lemma AI skill from github.com/uselemma/skills and use it to add tracing to this application.

SECURITY

Trust is non-negotiable.

Your data stays yours. Protected by best-in-class infrastructure and verified compliance standards.

SOC 2 Type IIEnd-to-end encryptionData Isolation

SOC 2 Type II

Independently audited security controls.

End-to-end encryption

AES-256 at rest, TLS 1.2+ in transit.

Data Isolation

Fully isolated per organization.

View Trust Center

Lemma Weekly

The Friday briefing on AI agent observability and reliability.

Latest — Issue 001 · July 17, 2026

Debugging production agents is moving earlier in the stack

A coding agent that leaked secrets from repository instructions; a managed runtime that hid injected logic from customer-visible telemetry; a workflow that reported success while one of its tools had already failed. Across all three, reconstructing what happened was easier than reconstructing why.

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