AI AGENTS FOR SRE & IT OPERATIONS
Bring incident evidence, investigation tools and runbooks into one agent-assisted workflow. Help engineers decide what to do next while keeping production changes under control.
Share your goals. We’ll help define the next project.
WHEN THIS WORK HELPS
For DevOps, SRE and Platform Engineering teams burdened by alert fatigue, fragmented monitoring dashboards, or looking to automate incident investigation.
SCOPE AND OUTPUTS
We agree on access, deliverables and checks for your environment.
Define read-only observability access via MCP (Model Context Protocol) and typed APIs, preventing unauthorized modifications to production infrastructure.
Deliverable: Scoped tool manifests, permission matrices and sandboxed execution runtimes.
Identify the logs, metrics and traces available in the current environment. Scope read-only access and test whether they support a useful incident timeline.
Deliverable: Source map, access plan and a reviewed timeline prototype.
Equip agents to follow diagnostic runbooks, query system status, verify health checks and propose verified hypotheses with concrete evidence.
Deliverable: Executable diagnostic playbooks and structured root-cause draft templates.
Present the exact proposed action, evidence and recovery plan in the team's approved channel before any production mutation.
Deliverable: Approval contract, audit record and tested rejection path.
HOW THE WORK PROGRESSES
Catalog current alerts, recurring incidents, monitoring tools and escalation paths to identify the highest-leverage diagnostic workflows.
Establish read-only API credentials and define immutable safety boundaries: agents may inspect, correlate and propose, but never commit writes unilaterally.
Build and test the investigation loop against historical incident logs to ensure accurate root-cause hypothesis generation.
Run the agent in shadow mode alongside on-call engineers, refining synthesis accuracy before enabling interactive notification workflows.
BEFORE WE START
Price, timing, access and support follow the agreed project scope.
No. By architectural design, our autonomous SRE workflows enforce read-only exploration. Any remediation command requires explicit interactive approval from a human engineer.
We review the APIs, access and data quality of the monitoring tools you already use. The first scope names the exact sources and operations to connect.
We show the underlying query, timestamp and result for each proposed finding, then test the agent against known incidents and require human review for consequential actions. No model can guarantee a correct diagnosis.
No. It can prepare evidence and possible next steps. The on-call team remains responsible for incident decisions and production changes.
The design should use scoped identities and keep credentials out of model context. The actual secret store, token lifetime and audit controls are agreed for the deployment environment.
START WITH THE CONTEXT
Primary guidance to review for your implementation:
START WITH YOUR QUESTION
Tell us what you want to improve. We’ll review your goals and discuss the next step.
“Our on-call team checks several dashboards for the same alert. Can we scope a read-only investigation and human review step?”
What happens after you send it?We review your description and reply with questions about your project. No calendar booking or newsletter signup.
No technical brief needed to start.