The Platform Debugger for Production
Sailfish gives engineers and AI agents debugger-level understanding of live production — capturing the evidence to fix today's bugs and prevent tomorrow's before customers report them.
AI can only fix what it understands.
When bugs happen in production, engineers and AI agents rarely get the actual execution context. They receive vague tickets, screenshots, partial logs, disconnected dashboards, and traces that only show what was predefined.
So teams add more logs, wait for deploys, wait for reproductions, and still hand AI agents incomplete context.
Sailfish solves the context problem.
Tickets are not evidence
Bug reports describe pain. They rarely contain the runtime facts needed to fix the issue.
Logs are predefined guesses
Logs only show what someone thought to capture before the bug happened. More logs also means more cost.
AI agents inherit the mess
Claude, Cursor, Codex, and internal agents can only act on the evidence they receive. Garbage in, garbage out.
A debugger for the whole customer-impacting system.
Traditional debuggers show execution state locally. Logs became the workaround for production. But logs are static, incomplete, and expensive.
Sailfish brings debugger-level understanding to live production systems.
It connects the customer experience, frontend behavior, backend execution, logs, exceptions, DB state and context, affected users, code context, and AI-agent handoff into one fix-ready debug packet.
Local Debugger
Deep insight — but only on your machine- Variablesinspect state at any breakpoint
- Inputs / outputswhat functions received and returned
- Execution pathstep through the exact code path
- Local-onlynone of it exists in production
Logs / Observability
Production coverage — only what you predefined- Predefined signalsonly what someone thought to log
- High noisesignals drown in the volume
- High costmore logs, bigger bill
- Missing contextno users, no workflows, no code
Sailfish
Debugger-level understanding, live in production- Zero instrumentationcaptured automatically — nothing to add, impossible to forget
- Runtime evidenceargs, state, and the failing path — captured live
- Affected userswho hit it, how many, how bad
- Workflow impactwhich customer flows are breaking
- Code contextthe code that ran, not just the trace
- AI-agent handofffix-ready packets for Claude, Cursor, Codex, and others
- Learns what to capture nextevidence gets sharper with every issue
Teams are asking for production evidence, not another dashboard.
Sailfish does not just summarize telemetry — it captures the missing evidence — giving humans and agents the production understanding needed to act.
Right now we send Jira tickets straight to Claude AI, but it doesn't have enough context to actually fix the bug. Sailfish could be a big way to package all of that context so AI can truly take action on it — that's super powerful.
When issue detection works at the workflow level — 'invoice batching is broken' instead of just 'accounting has a problem' — I no longer have to notify 15,000 users. I can notify the 1,000 who are actually affected.
Sailfish feels as if it's always on transparently — developers don't need to add separate log statements. It connects application log data, UI symptoms, and code together to find the root cause.
The most compelling capability is finding issues in the user flow before anyone reports them — that's essentially QA as a service, and that's exactly what I'm interested in.
Every issue teaches Sailfish what to capture next.
Sailfish learns from every issue — the important signals, the missing evidence, and the context engineers & AI used to reach the fix.
That knowledge compounds: Sailfish catches the same class of failure earlier every time — surfacing it in staging, in review, in the diff — until issues are stopped before they ever ship to customers.
Collect runtime evidence from real production behavior.
Reconstruct the failed workflow, affected users, and likely root cause.
Package fix-ready context for engineers and AI agents.
Remember what evidence mattered and what was missing.
Detect similar patterns earlier next time.
Bring us one production issue.
We'll show you what happened, who was affected, what evidence was missing, and what your engineers or AI agents need to fix it.