Enterprise Agent Governance Platform
AegisAI: govern agents before they change the business.
AegisAI is a deployed control plane for enterprise agent fleets. It intercepts agent tool calls through an AI Gateway, applies identity, RBAC, policy, risk, and human approval controls, then executes approved actions through a broker with signed audit evidence.
Start here to see the working deployed product, not just the case study.
Product thesis
Govern agents at runtime
Architecture
Gateway-first control plane
Risk posture
HITL, RBAC, kill switch, audit
Deployment
Vercel + Render + Supabase
Monitor
Fleet posture, lineage, tool activity, gateway coverage, risk heat, and incident signals.
Govern
Identity, RBAC, policy simulation, human approvals, and execution tokens before side effects.
Remediate
Kill switch, undoable actions, frozen agents, incident timeline, and signed audit packets.
Four-minute reviewer path
Evaluate the control boundary, not the screenshots.
The case study is intentionally falsifiable: reviewers can move from product behavior to code, tests, architecture decisions, and stated limitations.
Read the evidence standard- 01Open the live control plane and inspect fleet posture.
- 02Trace a side-effecting tool request through policy and risk evaluation.
- 03Inspect the human-approval boundary and bounded execution token.
- 04Cross-check the signed audit evidence, source implementation, and ADR.
Problem
Agent sprawl is becoming the next shadow IT problem.
Organizations are shipping agents into support, finance, analytics, content, engineering, and operations. The hard question is no longer whether an agent can complete a workflow. The hard question is who can prove what it did, why it was allowed, who approved it, what it cost, and how to stop it when it drifts.
What This Solves That Many Orgs Miss
Most teams optimize agent creation, not agent control.
Builder tools help teams create more agents, but they rarely answer who owns them, what tools they can call, when approvals are required, and how actions are audited.
Policy often lives in prompts.
Prompt-based instructions are not enterprise controls. AegisAI moves policy to a runtime gateway where decisions are explicit, inspectable, and enforceable.
Observability is treated as enough.
Logs and traces matter, but they do not stop a bad deploy or unauthorized refund. AegisAI combines monitor, govern, and remediate in one operating model.
Architecture Diagram
Gateway-first governance for live agents.
Production agent fleet
AI Gateway runtime intercept
Policy, RBAC, risk, HITL
Execution broker + connectors
Signed audit + control plane
Runtime Governance Flow
Agent requests a tool
Every side-effecting call enters the gateway contract before touching GitHub, Vercel, Render, Stripe, or another system.
Gateway evaluates authority
Kill switch, identity, RBAC, action type, risk tier, and policy rules decide allow, approval required, block, deny, or frozen.
Humans approve high-risk actions
Deploy, financial, and sensitive actions are routed through HITL queues instead of being delegated blindly to an agent.
Broker executes with a token
Approved work receives a bounded execution token and is executed through typed connectors, then written to audit.
Audit becomes evidence
Each decision and action is traceable through policy outcome, actor, tool, risk, connector, and signed audit payload.
Best Practices Encoded
- Runtime authorization before tool execution, not prompt instructions as policy.
- Separate orchestrator, policy, broker, connector, and audit boundaries.
- Use execution tokens for approved actions instead of long-lived agent authority.
- Keep human approval paths explicit for deploy, financial, and sensitive data workflows.
- Expose fleet-level posture and lineage so leaders can see agent sprawl before it becomes risk.
- Design FinOps and observability into the platform instead of adding them after production usage.
Platform Layers
Experience
Next.js control plane UI with Dashboard, Monitor, Governance, AI Gateway, Orchestrators, and Onboard screens.
Interfaces
FastAPI routes for gateway enforcement, control plane data, orchestrator triggers, and product modules.
Application
LangGraph website-build workflow, multi-agent orchestration, guardrails, RAG/vector memory, and execution broker.
Product
Agent registry, Agent Cloud posture, FinOps, audit export, policy simulator, Slack approvals, and kill switch.
Infrastructure
Supabase Postgres, Render API service, Vercel frontend, cron orchestration, notifications, and observability hooks.
Orchestrators Under Governance
AI Content Pipeline
Scheduled research and publishing workflow delivered through governed channels such as Slack and Telegram.
Stock Research
Weekday morning briefing pipeline with repeatable runs, notification delivery, and control plane visibility.
Website Build
LangGraph workflow: requirements analyst, UI design analyst, frontend engineer, backend engineer, and review/deploy gate.
Architecture Tradeoffs
Control plane over agent builder
I deliberately positioned AegisAI above individual agents. Enterprises already have agents emerging in many tools; the durable need is governance across them.
Gateway enforcement over passive observability
Trace dashboards are useful after the fact. AegisAI intercepts runtime action requests before side effects, so governance can prevent incidents rather than only explain them.
HITL for high-risk actions
Fully autonomous deploys and financial actions look impressive in demos but create unacceptable enterprise risk. The platform requires approvals where blast radius is high.
Simple deployment topology first
Vercel, Render, and Supabase keep the reference implementation deployable on a practical budget while preserving the same boundaries used in larger cloud environments.
Policy contract before connector growth
The platform treats policy, identity, and audit as the stable core, then lets connectors expand without changing the governance model.
Product Surface
Executive architecture across business risk, AI systems, and operating controls.
Dashboard, Monitor, Governance, AI Gateway, Orchestrators, and Onboard — each screen maps to a production concern: fleet posture, policy enforcement, approvals, and audit evidence.
Video walkthrough — 10 min (planned)
AegisAI: gateway-first agent governance walkthrough
Monitor → Govern → Remediate — runtime intercept, policy simulation, HITL queue, and signed audit on the live control plane.
Recording in progress. Subscribe on YouTube to get notified when this walkthrough publishes.
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Flagship agent governance case study.
Architecture, governance model, tradeoffs, and production operating mindset behind a deployed control plane — inspect the live platform, then read the ADRs on GitHub.