GRC that runs on agents, not on you
How an episki agent does work
You ask the agent to do something — in chat, in a workflow trigger, or via webhook.
The agent proposes a plan: a sequence of step-runs with the tools and data it will use.
Each step is a discrete, observable unit of work. Logged, replayable, debuggable.
Steps call native integrations or your MCP servers. Allowlisted per workspace.
Sensitive actions wait on human approval. Routed by policy, captured in audit log.
Hard limits the agent cannot exceed at the runtime level — not the prompt level.
Evidence pulls are deterministic, not AI-generated
A human asks for evidence (e.g., "MFA enforcement across all admin accounts"). The agent inspects your environment, drafts a deterministic recipe, and proposes it for approval.
Once approved, the recipe is plain, inspectable code. It runs on a schedule. No model in the loop. Same input → same output. Auditors can read the recipe and the run history end-to-end.
When an upstream API changes or a recipe fails, an agent investigates, proposes a fix, and waits for human approval. Operations don't drift — and you have a paper trail.
Agents with skills, not generic chatbots
Bring your own tools via Model Context Protocol
Hard limits the agent cannot exceed
- Allowlist exactly which MCP servers each agent can call
- Block destructive actions at the runtime, not the prompt
- Route every external-facing action through human approval
- Log every prompt, every tool call, every output — searchable + exportable
- Set workspace-wide ceilings on what data agents can leave the workspace with
- Pause agents instantly from a single workspace switch
Every agent action is auditable
Token economics
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