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Control Plane for Agentic DevOps & SRE

From automation to governed autonomous ops powering harness engineering

Context Lake gives agents and engineers the same live graph for monitoring, incidents, and governed change.

Built for agents · MCP · human operators

AI-native control plane
Git · CI/CD · K8s · Cloud · Integrations
Context LakeGraph + memory for agents
Console
Agentic Assistant
IDE · MCP
One graph · one policy model
Control plane for Agentic DevOps & SRE: Context Lake at the center with AI-aware operations, automation, governance, and integrations from AWS, GitHub, Snyk, Jira, Notion, Linear, and Confluence

Control plane for Agentic DevOps & SRE

  • Context Lake

    AWS, GitHub, Snyk, Jira, Notion, Linear, and Confluence data converge in one graph so operations and agents share current service context.

  • AI-aware operations

    Uptime and synthetics, incidents, on-call, status pages, and the service catalog connect to that context for triage and response.

  • Autonomous workflows and governance

    Governed autonomous ops: Day 2 actions run through policy, approvals, and an audit log—powering harness engineering, not ad hoc shell access.

One Context Lake — console, IDE agents, and agentic assistant share the same graph

Context Lake

Graph from Git, CI/CD, K8s, and cloud—memory for AI agents.

Agentic Assistant

MCP in Cursor and Claude Code—query, act, audit.

Operations

Reliability & Day 2—with agent context on every workflow.

Agentic SRE & DevOps

Uptime, incidents, on-call, and catalog grounded in Context Lake—governed autonomous ops for harness engineering.

Uptime & synthetics
Incidents
On-call
Status pages
Service catalog
Day 2 Ops

Autonomous Agentic Workflows

Agent-ready

Governed autonomous ops for harness engineering—approved Day 2 workflows (restart, scale, rotate secrets, trigger pipelines) with catalog context agents and operators share.

AWS, GitHub, Snyk, Jira, Notion, Linear, Confluence, and more feed the graph that powers agentic SRE & DevOps.

  • Describe intent in the IDE—agents map to catalog actions with guardrails
  • Move beyond ticket-driven scripts toward autonomous, policy-bound execution
  • Same policies whether a human clicks, a script runs, or MCP executes
Day 2 OpsAll integrationsAgentic Assistant

Toolchain → graph → agent or operator → action

AWS
GitHub
Snyk
Jira
Notion
Linear
Confluence
Context Lake
AI · MCPDescribe → act
ConsoleSelf-service
Governed automationRestart · scale · deploy · rotate
Policy check before every run

Any channel · same policy · full audit

EngineerConsole
DeployCI/CD hook
AI agentMCP
Policy engineChecks · approvals
Execute
Block
Audit trailRequester · approver · outcome
Agents stay inside guardrails

Governance / Audit

Agents under policy

Policy checks, approvals, and execution records on every change—console, deploy hooks, or AI-assisted flows in Cursor and Claude Code.

  • Agents propose; policy engine approves or blocks before execution
  • Separation of duties for high-risk targets and time windows
  • Who requested, who approved, what ran—evidence for humans and auditors
Governance & ComplianceAgentic Assistant

  • Describe intent

    Engineers or AI agents state what they need from the IDE; the platform resolves context via catalog lookup and live graph query.

  • Policy gate

    The policy engine evaluates intent against integrated sources (AWS, GitHub, Snyk, Jira, Notion, Linear, Confluence) and approves or blocks before any change runs.

  • Execute and audit

    Approved actions run as controlled operations (restart, scale, rotate secrets, trigger pipelines) with a full record of who, what, when, and evidence—the same rules for humans and AI.

Agentic DevOps governed end to end: engineer or agent describes intent, Context Lake lookup, policy approve or block, execution, and audit trail with the same policy for human or AI

Agentic DevOps — governed end to end

Teams using Exemplar

Venture-backed companies run production on the control plane

Engineering teams at high-growth startups use Exemplar for agentic SRE & DevOps and governed autonomous ops that power harness engineering.

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