Pavo

The hardest part isn’t fixing the agent.It’s knowing what to fix.

Pavo finds, validates, and prioritizes the gaps that should drive your next improvement cycle, complete with impact, evidence, and the likely lever.

  • 01Find gaps worth fixing
  • 02Size the blast radius
  • 03Identify the right lever
platform.pavoai.com/find-gaps

Find Gaps

Scan complete · 15,000 traces · 2 min ago

Surface recurring gaps

Production gap feed

19 gaps surfaced automatically

Last 30 days

Why production gaps stay hidden

A completed trace is not proof of a completed job.

No error fires. Every check passes. Four failure modes still drain resolutions, spend, and trust, here’s what they cost.

Intended behaviourProduction trajectoryReal outcome

What looked healthy

The assistant told the customer that their account changes were complete.

What Pavo uncovered

The write failed in the business application, creating repeat contacts and manual rework.

How Pavo found it

Compared the agent’s final claim, tool result, and downstream account state across similar sessions.

Claim checked against business state

Meridian support · last 30 days

2.6×

higher repeat-contact rate

Refund issuedState confirmed
Address updatedState unchanged
Order cancelledState confirmed
Ticket escalatedEvent missing

Evidence trail attached

One opportunity, traced

The production landscape scanned segment by segment, with every closed alternative still on the page.

02 · WHERE SHOULD WE IMPROVE?Opportunity DiscoveryPRODUCTION LANDSCAPEUSERS × OUTCOMES × MODEL VERSIONSWORKFLOWS × COHORTS2.4M SESSIONS · 90 DAYS · SUPPORT AGENTOPEN GAPDISMISSEDPROMOTEDGAP · SURFACED99 SEGMENTS · 10 UNDERPERFORMING · 6 DISMISSED · 3 OPEN · 1 PROMOTEDOPPORTUNITY TRAILOPP-142VALUE AT STAKE£310K / yrAFFECTED18% of high-intent usersSUSPECTED MECHANISMglobal cap suppresses sendsEVIDENCEstable across 6 weeksALTERNATIVES RULED OUT3 CLOSEDSeasonalityNO SIGNALLatency regressionNOT CORRELATEDTraffic mix shiftCONTROLLED FORINTERVENTION DIRECTIONintent-aware fatigue policyCONFIDENCE0.83NEXT · BUILD BENCHMARKEVIDENCE TRAIL ATTACHED PER LINE · SELECT TO INSPECT

Peer-reviewed at NeurIPS, ICLR, KDD, WWW, SIGIR, and WSDM. Knowing what to fix is a research problem before it is a product one.

  1. How Task Structure Limits Multi-Agent Success: An Information-Theoretic AnalysisS. Pan, M. LuoOpenReview 2026
  2. Monitoring the Evolution of Behavioural Embeddings in Social Media RecommendationS. Saket, O. Jeunen, D. KalimSIGIR 2024
  3. Quantifying and Leveraging User Fatigue for Interventions in Recommender SystemsH. Sagtani, M. Jhawar, A. Gupta, R. MehrotraSIGIR 2023
  4. Counterfactual Evaluation of Slate Recommendations with Sequential Reward InteractionsJ. McInerney, B. Brost, P. Chandar, R. Mehrotra, B. CarteretteKDD 2020
View all research

A gap is a diagnosed and sized opportunity, not a trace, an anomaly, or a quality score. It names a difference between what the system was meant to do and what production actually did, at the level a fix would land on.

Each one arrives with its evidence, the sessions and segments it touches, and the lever most likely to close it.

Rules catch conditions someone already knew to define, and trace explorers help investigate problems someone already suspected. The gaps that cost the most stay invisible because nothing throws an error: the agent completes the task, the trace looks clean, and the outcome is still wrong.

Pavo works from the production trajectory and the real outcome rather than from the error log, so a completed trace is not taken as proof of a completed job.

A candidate has to survive validation before it earns a place on the list. Pavo confirms it reproduces, separates it from one-off user behaviour, and discards patterns that exist only because of a single retry loop.

What survives is grouped by what actually went wrong, wrong tool call, missing context, bad grounding, an unrecoverable handoff, rather than by error string.

By the blast radius: how many sessions a gap touches, which user segments and surfaces it reaches, and what it costs downstream in retries, escalations, and abandoned tasks.

That is what lets the queue be ranked by expected impact rather than by frequency, which is what makes the most common failure and the most expensive one distinguishable.

Each gap carries the lever it points at, prompt, tool contract, retrieval, model choice, or the surrounding system, with the trace evidence that points there.

From there the loop continues: turn the gap into an eval so the fix is measurable, test the intervention against it, and compound the result back into system knowledge.

Production traces. If your agent already emits them, Pavo can work from that history without migrating your stack or instrumenting the application again, and it runs alongside your existing observability tools rather than replacing them.

How much history is useful depends on the complexity of the agent and the outcomes being measured. Talk to us and we’ll size it against your system.