You cannot fix what you cannot see.The observability stack we ship to every client.
Production agents fail silently without observability. Per-step traces, score-bearing evals, cache-hit telemetry, tool-selection score deltas — these are the primitives that let you debug agents instead of guessing at them. We design and wire them in from day one.
What agents you have, what they do, what you can already see, what is dark.
02
Wire the four layers
Traces, eval framework, cache telemetry, selection telemetry. Each layer is wired and validated independently.
03
Build the eval set
Adversarial cases per workflow — not happy paths. Score every layer at the agent boundary.
04
Cost attribution
Per-agent / per-workflow / per-customer cost flows into the dashboards. The "AI bill" becomes auditable.
05
Alert + run-book
Threshold-based alerts on drift, error rate, cost. Run-books linking each alert to a known mitigation.
Security & scalability
Observability without leakage
PII-aware traces
Sensitive inputs are redacted or hashed before reaching the trace sink. Replay still works against synthetic stand-ins.
Tenant-scoped dashboards
Multi-tenant observability — your dashboards never see another tenant's traces.
Sampling vs. completeness
High-stakes workflows trace 100%. Low-stakes traffic samples to control cost. Decisions are explicit, not accidental.
Integrations
Observability integration points
Langfuse (cloud or self-hosted)
Datadog · Honeycomb · New Relic · custom OTel collectors
PagerDuty · Opsgenie for alerting
Slack · Microsoft Teams for run-book triggers
PostgreSQL / ClickHouse / BigQuery for analytical store
Business impact
What observability tends to unlock
Most teams discover surprising things in their first two weeks of proper observability — cache-hit rates lower than they assumed, cost concentrated in two routes nobody flagged, eval scores worse on edges nobody tested.
30–50%
token cost reduction surfaced by cache telemetry
~70%
of debugging time recovered with per-step traces
< 2 wks
to wire observability into an existing agent system
Case studies
How recent engagements actually shipped
IT Services · 6 weeks discovery → handoff
PR review pipeline cuts senior-engineer time 4×
Mid-market IT services firm · Ahmedabad · 180 engineers
Problem
Senior engineers were spending 8–12 hours per week each on first-pass PR review across a 6-team monorepo. Junior PRs waited 2+ days for sign-off; velocity stalled; the highest-judgement people were doing the lowest-judgement work.
Solution
A multi-agent CI workflow triggered on every PR open. Three specialist agents run in parallel — a reviewer (Claude Sonnet 4.6) for code-correctness and convention, a security agent for risk patterns, and a test-generator agent for coverage gaps. Outputs are consolidated into a single PR comment within 90 seconds. Humans review the agent's synthesis, not the raw diff.
Claude Sonnet 4.6 (reviewCustom MCP server: GitHub APIGitHub ActionsLangfuse traces
~36 hrs/wk
senior engineer time reclaimed across the team
< 3 days
payback period at loaded-cost rate
4×
review throughput per senior engineer
0
production regressions traced to AI-passed reviews in 90 days
Audit-grade compliance review ships under multi-layer guardrails
Regulated financial-services intermediary · India · 95 employees
Problem
Manual compliance review of vendor and onboarding documents was the bottleneck for new-customer activation. Every traffic spike threatened SLA breach. Reviewer fatigue led to inconsistent flagging — some weeks too strict, some weeks too loose, with no defensible pattern.
Solution
A single-agent system wrapped in four guardrail layers: an input filter that detects and redacts PII / strips prompt-injection patterns; a versioned policy registry the agent must cite by clause ID for every conclusion; output validators (schema + LLM-as-judge cross-check); and a human-in-the-loop gate on anything scored above a defined risk threshold. Every decision is appended to an immutable audit log.
Custom detectorsClaude Opus 4.7 (final ruling)Versioned in repoPydantic v2
Multi-agent research synthesis — open PoC for swarm vs supervisor
Public R&D · open-source on GitHub
Problem
Every team building multi-agent systems faces the same orchestration question and answers it from intuition, not measurement. "Supervisor is cleaner" vs "swarm is faster" gets stated as fact in a hundred conference talks without a single side-by-side benchmark anyone can reproduce. This PoC builds and measures both, on a task with a defensible ground truth.
Solution
A reproducible benchmark: same task (synthesise a literature review across 12 papers on a given topic), same model, same MCP tool registry, same eval rubric. Three runners — single-agent (baseline), supervisor pattern, swarm pattern — each scored on factuality, citation accuracy, coverage, and cost. Code + eval data + raw runs all open-sourced.
Claude Sonnet 4.6 (all three runners use the same model)Custom MCP servers: paper-fetchThree parallel implementations sharing the same tool registryOpen eval rubric