Enterprise Agentic RAG Copilot + Incident Commander

A permission-aware agentic copilot that assists incident response.

This system is planned. The case study describes the intended design; metrics are targets or planned, not measured results.

Problem

Naive RAG agents retrieve across everything and execute tools directly, leaking documents across tenants and taking risky actions with no audit trail or approval step.

Why this matters

Enterprises adopting agents need permissioned retrieval, guarded tools, and audit. That governance layer is what decides whether agentic products are actually shippable for AI platform vendors and their enterprise customers.

Constraints

Retrieval must enforce per-document and per-tenant permissions.
Risky tools require explicit human approval.
Every tool call and decision is logged for audit.
Prompt-injection is treated as an expected attack, not an edge case.

Architecture

Interface

Copilot UI
Approval queue

Agent

Planner
Guarded tools
Injection defenses

Retrieval

Permission filter
Vector + keyword search

Audit

Decision log
Trace by incident ID

A planner coordinates permission-filtered retrieval and a guarded tool layer; high-risk tools route through a human-approval queue; all steps emit an audit trail correlated by incident ID.

Data flow

A query is planned, retrieval is filtered by permissions, tools run (risky ones pause for approval), and every step is written to an incident-correlated audit log.

Control plane vs data plane

Control: Approval queue, policy for which tools are risky, and the audit log.

Data: The agent loop: planning, permission-filtered retrieval, and tool execution.

Core capabilities

Permission-aware retrieval evaluated at query time.
A tool layer with an approval gate for high-risk actions.
Incident workflows: triage, summarize, propose mitigations.
Full auditability of what the agent saw and did.

Staff-level tradeoffs

Human approval for risky tools instead of full autonomy.
Auditable, safe-by-default behavior matters more than a flashy fully-autonomous demo.
Permissions enforced at retrieval, not just in the UI.
Tenant isolation has to hold at the data boundary; UI checks are not security.

Tech stack

Frontend

Next.js
React
TypeScript

Backend

Python
LLM tool layer

ML / Data

Vector search
hybrid ranking

Infrastructure

Kubernetes

Observability

structured audit logs
traces

Metrics

Cross-tenant retrieval leakage: 0 (permission-filtered)
Risky actions without approval: 0 (approval-gated)
Prompt-injection test suite: Baseline + regression cases
Retrieval p95 latency: Bounded for interactive use

Metrics are labeled measured, target, or planned. Nothing here is an achieved result unless it is marked measured.

Failure modes

Prompt-injection attempt
Blocked by injection defenses and recorded in the audit log.
Over-broad retrieval request
Permission filter denies out-of-scope documents before the model sees them.
Risky tool invoked
Held in the approval queue until a human approves; nothing executes silently.

What's next

Stand up permission-filtered retrieval with a small governed corpus.
Add the approval queue and injection test suite.