FLEECE / AI BRAINYou cannot govern what nobody has counted.
Sales stood up a prospecting agent. Engineering wired a review bot. Someone in ops has three automations running on a personal API key. Nobody has a single list of what exists, what it costs, or who is accountable when it does something wrong.
Fleece AI Brain gives operations and IT one live map of the company's AI: every agent, tool, integration and human on a single knowledge graph, each agent node showing its model, token spend, run rate and named human owner. Underneath, ServiceNow, Asana, SharePoint, Google Drive, Box and Dropbox sync into the same local-first vault, so the map sits on top of the systems you already run.
Shadow AI is an inventory problem before it is a risk problem
AI adoption inside companies did not arrive through procurement. It arrived team by team, on individual credit cards and personal API keys, and the org chart of who runs what has never been written down. That is manageable at three agents and unmanageable at thirty.
The cost side has the same shape. A cloud bill tells you what the company spent on inference; it does not tell you which agent burned it, whether that agent produces anything, or who to ask. By the time finance queries a line item, nobody can reconstruct which workflow it belonged to.
- An agent keeps running after its owner leaves, because nothing recorded that they owned it.
- Inference spend triples in a quarter and no one can attribute the increase to a specific workflow.
- A compliance review asks which systems process customer data, and the answer takes three weeks to assemble.
- Two teams build the same automation because neither could see the other's.
One inspectable map, with costs attached
The Brain builds the map from what you already operate: connect your tools, teams and agents, and it produces a live graph of every AI agent, tool, integration and human, organised around one company brain. Open any agent node and you get its model, token spend, run rate and the human accountable for it — the inventory that a governance conversation needs before it can start.
Underneath, the same connectors that serve knowledge also serve operations: ServiceNow for the service desk, Asana for the work, SharePoint, Google Drive, Box and Dropbox for the documents. Everything lands in a local-first vault of plain Markdown on machines you control, and on the Teams plan it comes with role-based access, an audit log and SSO — the controls that usually decide whether a tool like this passes review at all.
The connectors operations teams start with
One sign-in each from the desktop app; every connector page states exactly what it syncs.
ServiceNow
Connect ServiceNow to Fleece AI Brain: every Knowledge Base article becomes a note in a local-first knowledge base your AI apps query over MCP.
See what it syncs →Asana
Connect Asana to Fleece AI Brain: every assigned task's name and notes become a note in a local-first knowledge base your AI apps query over MCP.
See what it syncs →SharePoint
Connect SharePoint to Fleece AI Brain: every document becomes a note in a local-first knowledge base on your own machine, queried over MCP.
See what it syncs →Google Drive
Connect Google Drive to Fleece AI Brain: Docs, Sheets and text files become notes in a local-first knowledge base your AI apps query over MCP.
See what it syncs →Box
Connect Box to Fleece AI Brain: every file's text becomes a note in a local-first knowledge base your AI queries over MCP. Governed content, finally findable.
See what it syncs →Dropbox
Connect Dropbox to Fleece AI Brain: every file's text becomes a note in a local-first knowledge base your AI queries over MCP. A decade of files, searchable.
See what it syncs →What operations leaders weigh this against
The usual alternatives are an enterprise search layer or a wiki — both of which index documents but neither of which inventories agents.
Worth reading first
The pieces ops and IT leads tend to read before an internal proposal.
AI Agent Sprawl: How to Audit Every AI Agent in Your Company (2026)
AI agent sprawl is the uncontrolled accumulation of AI agents, copilots and automations across a company, with no central inventory of what exists, who owns it, or what it can touch. The fix is a seven-step audit: inventory the obvious seats, hunt shadow agents, map owners and data scopes, meter cost, consolidate memory, prune, and set a review cadence. Done right, the output is not a spreadsheet that decays — it is a living AI Organization Map. Here is the exact process we use.
AI Agent Governance: The Complete 2026 Guide
AI agent governance is the discipline of keeping every AI agent in your company accountable: a registry of what exists, explicit permissions and scopes, per-agent cost attribution, governed memory, and an audit trail. In 2026 — with agents acting autonomously across real systems and regulators paying attention — it has become as basic as access control. This guide covers the five pillars, a step-by-step rollout, and the tools landscape, including where Fleece AI Brain fits as the org-map, cost and memory layer.
The 8 Best LLM Cost Tracking Tools in 2026
The best LLM cost tracking tool depends on who is asking: engineers debugging an expensive chain want observability tools like Helicone, Langfuse or LangSmith; a business wanting one honest map of every agent and what it costs wants an org-level meter. We rank Fleece AI Brain first for company-wide per-agent cost and budgets — and most teams run one of each.
How an operations team starts
- 01
Inventory before you govern
Map what is already running — every agent, who owns it, what it touches. A policy written before the inventory governs an imaginary estate.
- 02
Attach a named owner to every agent
An agent without an accountable human is the one that keeps running after a reorg. Ownership recorded in the graph is ownership that survives the next one.
- 03
Connect the systems of record
ServiceNow, Asana and your document stores put operational context in the same graph as the agent map, so an agent node links to the process it serves rather than floating alone.
- 04
Turn on the controls before you scale
Role-based access, audit log and SSO on Teams, plus the Janitor autopilot to keep the graph clean unattended. Controls added after sprawl are always retrofits.
Operations & IT questions
How does the Brain know which AI agents exist?
You connect your tools, teams and agents, and the map is assembled from what you already operate rather than from a spreadsheet someone maintains by hand. Each agent becomes a node you can open to see its model, spend and owner.
Can we see what each agent costs?
Yes — per agent, not per cloud bill. An agent node reports its model, token spend and run rate, which is what makes it possible to tell a workflow that earns its cost from one that quietly burns budget.
Where does the data physically live?
On your machines. The vault is plain Markdown with a local SQLite index, connector content travels from each provider straight to the device, and sync between your own machines is opt-in and end-to-end encrypted. There is no requirement to park company knowledge in our cloud.
What access controls are available?
The Teams plan adds cross-user sharing with role-based access control, an audit log, and SSO through Google or Microsoft. Solo and Pro are single-vault plans without those controls.
Does this replace our ITSM or our document management?
No. ServiceNow, SharePoint and the rest stay the systems of record; the connectors sync from them so their content joins the graph your AI tools query. What the Brain adds is the agent map and the shared memory layer above them.
How much does it cost for an operations team?
Teams is €49 per user per month and is the plan with RBAC, audit log and SSO. Solo is €12 and Pro €24 for smaller setups. Every plan starts with a 14-day trial, cancel anytime.
Other teams
Count the agents. Then govern them.
Map what is already running during the trial, and see the bill by agent instead of by month.