Alert fires
The signal is noisy, severity is uncertain, and customer impact is still unclear.
For on-call SRE and platform teams
Stop losing incident time to context hunting.
InfraOps Agent Hub turns cloud alerts, logs, release context, and runbooks into a first-response packet: what fired, what changed, what runbook applies, and what needs human approval.
Clear promise: this is triage and handoff first. Production-impacting actions stay blocked until a human approves them.
The incident handoff problem
A 5xx alert should not become a Slack scavenger hunt.
When an alert fires, the on-call engineer has to jump between metrics, logs, deploy history, runbooks, Slack, and tickets while people are asking for answers. The facts are scattered, the timeline is fragile, and the safest next step gets mixed with risky guesses.
Context splits
One person checks logs, another checks releases, and runbook guidance sits in a different place.
Risk rises
Rollback, restart, scale, and config-change ideas appear before evidence and approval are captured.
Documentation lags
The audit trail gets reconstructed later from Slack threads, terminal history, and memory.
What changes
Give the responder one packet they can trust.
InfraOps Agent Hub gives the on-call engineer a structured path from noisy alert to safe handoff. It gathers approved context, separates evidence from recommendations, blocks risky actions until approval, and keeps an audit trail while the incident is fresh.
- Start with the alert. A webhook or sample payload becomes one incident context.
- Attach evidence. Logs, release timing, and runbook guidance are gathered into the same view.
- Hand off safely. The plan separates read-only checks from approval-required actions.
Operational outcomes
Less chaos during incidents. Better proof afterward.
Cut triage drift
Keep alert facts, sampled logs, release correlation, and runbook matches together so responders argue less about what happened.
Reduce unsafe automation
Classify actions as read-only, approval-required, or blocked before anyone treats a recommendation like a command.
Improve handoffs
Give the next engineer a short, evidence-backed incident summary instead of a long Slack thread.
Make audits easier
Write incident evidence, recommended actions, and approval state into Postgres while the incident is still fresh.
Protect runbook discipline
Ground next steps in approved runbooks and make unsupported actions visible before they become production changes.
Evaluation workflow
Three steps from alert to operator handoff.
The evaluation build includes a runnable local demo and an importable n8n workflow. Real cloud, Slack, GitHub, and LLM calls stay disabled until least-privilege adapters and approval checks are in place.
Receive the incident context
The demo starts from an InvoiceBridge 5xx alert and reads structured sample logs plus the high-5xx runbook.
Produce the first response packet
Evaluation-mode outputs summarize triage, release correlation, runbook lookup, next-step planning, and documentation.
Gate action and preserve evidence
Production-impacting steps remain approval-required. The local demo inserts one audit event into Postgres.
Concrete starting points
Built around incidents platform teams already handle.
5xx spike after a deploy
Correlate error logs with release timing, select the high-5xx runbook, and prepare an approval request before rollback.
ECS service saturation
Summarize CPU pressure, request trends, and safe read-only diagnostics before scaling or restarting workloads.
RDS storage pressure
Capture projected exhaustion, growth signals, and blocked actions before any retention or cleanup change is considered.
CI failure after dependency changes
Turn workflow failure context into a follow-up issue without giving automation broad repository write access.
Why this exists
Not another generic chatbot.
Why not just use ChatGPT?
Generic chat does not know your approved runbooks, does not enforce approval gates, and does not write an incident audit record by default.
Why not use spreadsheets?
Spreadsheets can track incidents after the fact, but they do not guide the first responder through evidence, risk, and approval during the incident.
Why not just use Zapier or n8n?
n8n is the workflow engine here. InfraOps Agent Hub adds incident prompts, runbook structure, approval policy, sample data, and audit schema.
Why not keep doing it manually?
Manual response works until the incident is noisy, the Slack thread grows, and the audit trail depends on someone writing it up later.
Truthful trust signals
Designed for cautious teams, not blind automation.
- Local-first evaluation build with Docker Compose for n8n and Postgres.
- No real AWS, Slack, GitHub, or LLM calls in the importable workflow.
- No real secrets committed to the repository.
- Approval-required and blocked action classes are documented in runbooks and schema.
- Real integration path documents least-privilege setup, redaction, and cost controls before production use.
Private evaluation
See whether this fits your incident workflow.
Request a walkthrough with your alert sources, runbooks, and approval constraints in mind. There is no public billing or self-serve signup yet.
This opens a prefilled GitHub issue for now. No payment is collected. Do not include secrets, tokens, or private incident data.
FAQ
Questions a platform buyer will ask.
Who is this for?
On-call SRE, platform, DevOps, and infrastructure teams that handle cloud incidents and need a safer AI-assisted triage loop.
How is this different from generic AI tools?
It is built around incident evidence, runbooks, approval gates, and audit records instead of open-ended chat.
What setup is required?
The evaluation build runs locally with Docker Compose. The included n8n workflow imports without credentials.
Is my data safe?
The demo uses local sample data only. The production path calls for server-side secrets, redaction, least privilege, and audit writes before external side effects.
What happens after I request access?
The current flow opens a GitHub issue for a walkthrough request. A private intake form and in-app signup are not implemented yet.
What does this replace?
It can reduce ad hoc triage docs, scattered Slack handoffs, and manual audit reconstruction. It does not replace monitoring or incident command.
What does this not do yet?
It does not call real cloud APIs, send Slack messages, create GitHub issues, run LLM inference, or execute production remediation.
Can it run cheaply?
Yes for evaluation. The deployment guide recommends one small VPS or Lightsail instance before using RDS, ALB, ECS/Fargate, or OpenSearch.