AI Agent Management Platform
As organizations move AI agents from prototype to production, a familiar problem appears: nobody can say exactly how many agents are running, who owns them, what changed last week, or whether the last change made things better or worse. AI agent management is the discipline — and the tooling — that answers those questions.
What is AI agent management?
AI agent management is the practice of keeping every AI agent in an organization inventoried, owned, versioned, governed and measured across its entire lifecycle — from the first prototype to the version running in production today.
It is the agent equivalent of what source control plus CI/CD did for application code. An agent is not just code: it is a model, a system prompt, a set of tools, and parameters. Any one of those can change behaviour dramatically, and most of them change outside your Git history — in a prompt file, a config toggle, or a conversation.
Why enterprises need it
A single prompt edit can regress quality for thousands of users with no compile error and no failing test. A model upgrade can quietly change tone, latency and cost at once. Because these failures are silent — the code runs perfectly, the outputs simply get worse — traditional monitoring never fires.
Enterprises also face a governance question: who is allowed to change an agent, who approved the change that reached customers, and can you prove it six months later during an audit?
The core capabilities
- Agent inventory — a single list of every agent under management, with its current live version.
- Ownership — a named owner for every agent, so no agent is orphaned.
- Version control — each change becomes an immutable, content-hashed version you can diff and roll back.
- Permissions and access — least-privilege roles governing who can write, deploy, approve or override.
- Lifecycle management — a clear path from draft to staging to production, and back again via rollback.
- Governance — deterministic policy applied before a version ships.
- Monitoring — success rate, latency, drift and error behaviour after it ships.
- Auditability — an append-only record of who did what, when, and why.
- Cost control — token and spend attribution per agent and per version.
- Risk management — blocked deploys, reason-required overrides and restricted-data guards.
How Raweki approaches AI agent management
Raweki treats the agent definition as the unit of record. Every commit produces a numbered, hashed version; every promotion is a recorded deployment; every run your agent reports becomes evidence attached to that version.
Crucially, Raweki never runs your agents. Your agents keep executing wherever they already do — LangChain, LlamaIndex, a raw SDK loop, or a bespoke stack. Raweki sits beside your runtime as a control plane, which means zero added latency, no single point of failure in your request path, and no framework lock-in.
Where to go next
AI agent governance →AI agent versioning and diffs →AI agent observability →What is an AI workforce? →
Frequently asked questions
What is an AI agent management platform?
A system that keeps every AI agent inventoried, owned, versioned, governed and measured — covering agent inventory, access control, deploy gating, audit trails and cost, across the whole agent lifecycle.
How is AI agent management different from LLM observability?
Observability tells you what happened after a change shipped. Agent management also records what changed, enforces checks before a change ships, and lets you roll back — it is a system of record plus an enforcement point, not only a dashboard.
Does Raweki run or host our agents?
No. Raweki stores each version's definition and scores the run metadata you report. Your agents run entirely in your own environment, so there is no added latency and no framework lock-in.