Collaborate with agents to resolve incidents with evidence.
Every theory runs in parallel, human and agent working the same investigation, steerable at any point. 89% root-cause accuracy, benchmarked, not asserted.
Today · Tue, Jul 7
HighHttp5xxErrorRate: checkout-api 5xx error rate 8.2%
Payment validation rejects gold loyalty checkouts with 'Invalid token', cascading HTTP 500s
checkout p99 latency above 200ms budget for 10m
No root cause found: PostgreSQL operating normally, alert was a false positive
TraefikHighErrorRate: Traefik 5xx error rate 10.7%
Cluster nodes overcommitted memory beyond capacity, causing critical memory pressure
Recommendation-service overnight error rate within normal seasonal range, no action needed
The answer lives in someone's head.
Whether it is a 3am page or a Tuesday-afternoon 'is this normal?', the same friction shows up: the context is scattered, the person who actually knows is busy or gone, and now AI agents are firing off their own investigations with no shared memory to draw on.
Our take: Your service catalog is already wrongEvery question starts from zero
Which dashboard, which query, which service owns this. Most of an investigation is just gathering context, and every engineer, and every agent, re-gathers it from scratch.
Knowledge is tribal
The fastest path runs through whoever debugged it last time. When they are on PTO or they leave, that knowledge walks out the door, and the error budget pays for it the next time it recurs.
Questions that cannot wait for an incident
'Is this latency expected? What changed here?' rarely justifies pulling a war room, so it goes unasked and unanswered until it becomes a 3am page.
One question, answered with evidence.
Someone asks why checkout is failing. Here is what NOFire does against a live model of your production, before anyone opens a dashboard.
Payment rejects gold loyalty checkout
A payment deploy started rejecting gold loyalty checkout requests with an ‘Invalid token’ error, cascading to checkout card-charge failures and HTTP 500s at the frontend. Root cause: validation logic fails for the app.loyalty.level=gold attribute.
main-7c41a9
Deploy main-7c41a9 shipped payment validation that rejects app.loyalty.level=gold tokens as invalid
Payment service returns rpc.grpc.status_code=2 (UNKNOWN) with 'Invalid token' to checkout
Checkout charge requests fail with 'could not charge the card: rpc error'
Frontend returns HTTP 500 to clients, cascade complete in 51ms across 3 services
Alert fired: 7,627 of 30,560 checkout RPC requests failing with status_code=13
Ask in plain language
“Why is checkout failing?” NOFire explores knowledge, metrics, logs, and traces across 16 queries automatically. No dashboard to pick, no query language to write.
Trace the causal chain
It pins the exact change that started it: a payment deploy began rejecting gold loyalty-level requests with an 'Invalid token' error, cascading through checkout to the frontend in 51ms. 7,627 of 30,560 checkout RPC requests failed.
See the blast radius and timeline
A live impact map shows the three affected services (payment as origin, checkout direct, frontend cascading), and an event-sequence timeline orders the change, symptoms, impact, and detection. Nothing to reconstruct by hand.
Every hypothesis is scored, cited, or ruled out.
NOFire does not hand you a single guess. It ranks the likely causes, backs the leading one with cited signals, and keeps the alternatives it rejected on the record so you can trust the answer.
Payment deploy main-7c41a9 introduced validation that rejects gold loyalty tokens as invalid, cascading card-charge failures through checkout to the frontend.
- Deploy main-7c41a9 shipped payment validation 3 min before the first failure
- Payment returns rpc.grpc.status_code=2 (UNKNOWN) with 'Invalid token'
- Failing requests all carry the app.loyalty.level=gold attribute
- Non-gold checkout traces succeed in the same window
- Error propagates payment -> checkout -> frontend in 51ms
- 7,627 of 30,560 checkout RPC requests failed with status_code=13
- Past investigation key=9f21c0 documents an identical gold-tier validation regression
Ranked, not a single guess
The most likely hypothesis lands at 85% confidence with the gold-loyalty deploy named as the cause, while two alternatives are scored at 15% and 10% and shown as rejected.
Every signal cited
Each supporting signal is listed and checked: the deploy timing, the gRPC status, the loyalty-tier attribute, the 51ms cascade, and a prior investigation with the same fingerprint. No black-box verdicts.
Alternatives ruled out on the record
A malformed-token theory and a retry-storm theory were both considered and rejected with reasons, so nobody re-litigates them at 3am.
Every investigation makes the next one smarter.
An answer that disappears when the tab closes is worth less. When you conclude an investigation, NOFire turns what it found into reusable knowledge by opening a knowledge pull request, reviewed by you, and feeds it back into your production context.
Knowledge PR
NOFire drafted 4 entries from INV-14 · Payment rejects gold loyalty checkout. Review and merge into your production context.
Payment deploy main-7c41a9 rejected gold loyalty checkouts with 'Invalid token', cascading HTTP 500s across 3 services in 51ms.
Validation must accept every loyalty tier. Gold tokens were treated as invalid (gRPC status 2). Reproduce with a gold-level token before release.
Depends on payment for card charges. Add retry with backoff and a circuit breaker so payment errors do not cascade to the frontend.
Add test cases covering all loyalty tiers against the real token format each tier uses, and gate payment deploys on the suite.
Conclude into a pull request
When the root cause is confirmed, NOFire drafts what it learned as a knowledge pull request: an incident summary, one entry per affected service, and a recommended runbook, each as a versioned file.
Review it like code
Approve or skip each entry individually. It reads like a diff, with the exact additions proposed, so nothing lands in your production context without your sign-off.
Merge, and it is live
Approved entries merge into the Production Context Graph and the knowledge library, tagged to the services they belong to and ready for the next investigation, human or agent.
Tribal knowledge, finally written down.
The fix that used to live in one engineer's head becomes a versioned, searchable asset. Every concluded investigation adds to a knowledge library the whole team, and every future investigation, can reuse.
Knowledge library
Curated runbooks, incident summaries, and investigation patterns your team can reuse.
Payment Service Runbook
Repository: github.com/NOFireAI/opentelemetry-demo · Team: @payments-team · On-call: #payments-oncall · SLOs: p99 < 200ms, error rate < 0.1%, 99.9% availability. Dependencies upstream: checkout-api, order-service.
Frontend service, investigation guidance
The frontend sits behind the frontend-proxy and is the entry point for all user requests. Errors on the frontend frequently originate from downstream services (auth, checkout, payments), not from frontend code itself. Do not treat frontend errors as root cause.
Gold loyalty checkout regression
Validation logic in payment must accept all loyalty tiers. Reproduce with an app.loyalty.level=gold token before regression testing. Prior incident INV-14: gold tokens rejected as invalid, cascading HTTP 500s in 51ms.
Knowledge becomes a first-class asset
Approved items land in a versioned library as runbooks, incident summaries, and investigation patterns, tagged to the services they belong to and searchable by anyone, not buried in a thread.
The next investigation starts ahead
Because knowledge writes back into your production context, the next question about payment or checkout begins with everything your team already solved. The gold-loyalty regression is now a runbook, not a rediscovery.
Reactive tools hand you more data. NOFire hands you the root cause, and remembers it.
- More dashboards and alerts, but no answer
- Every incident re-investigated from a blank page
- The fix lives in one engineer's head, and walks out with them
- A cited, confidence-scored root cause in minutes
- Every investigation writes back to the Context Graph
- The same class of incident never costs you twice
Every investigation runs against the live model of your services, dependencies, ownership, and change history, and writes what it learns back into it.
Answers that compound, not evaporate.
Ask anything, anytime
Ad-hoc questions get the same rigorous investigation as a 3am page, no incident required to start.
No blank dashboards
Investigations open with metrics, logs, traces, and deploys already gathered and correlated for you.
Evidence you can verify
Every hypothesis is scored and cited back to the production signal it came from, never a black-box answer.
Knowledge that stays
Concluded investigations become reviewed runbooks and summaries, so tribal knowledge outlives the person and the tab.
Owned by your team
You approve what becomes knowledge, which keeps the library accurate and worth trusting.
Smarter over time
Each answer feeds your production context, so every future investigation, human or agent, starts further ahead.
AI writes the code. NOFire keeps it running.
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