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Control Plane for Agentic DevOps

AI-native platform for post-launch ops—Context Lake gives agents and engineers the same live graph for monitoring, incidents, automation, 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: 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

  • 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.

  • Automation and governance

    Day 2 actions (restart, scale, rotate secrets, trigger pipelines) run through policy, approvals, and an audit log—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.

AI-aware operations

Uptime, incidents, on-call, and catalog—all grounded in Context Lake so agents and automations never fly blind.

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

Autonomous Agentic Workflows

Agent-ready

Run approved Day 2 workflows on demand—restart, scale, rotate secrets, trigger pipelines—with catalog and integration context agents can use.

AWS, GitHub, Snyk, Jira, Notion, Linear, Confluence, and more feed the graph that powers automation and MCP tools.

  • Describe intent in the IDE—agents map to catalog actions with guardrails
  • React to deploys, incidents, and signals from connected tools
  • 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 reliability, governed Day 2 Ops, and AI-native operations.

Rigi TVSharpsellFundsIndia