Platform and integration
Action lake
An action lake is a governed layer between AI agents and business systems where every action an agent may take is defined, every action it takes is authorised and priced, and every action it took is recorded, independent of the model, framework or vendor.
What it is
A data lake, in AWS's definition, is a centralised repository that lets you store all your structured and unstructured data at any scale. It gave analytics one place to look instead of a copy of every source in every tool. An action lake applies the same idea to what agents do rather than what they read: one place where actions are declared, permitted, priced, run and recorded, whatever model or framework an agent uses.
The term is ours; actionlake is named after it.
Why it matters
Every agent project starts by connecting the same systems again: new credentials, new tool definitions, new guesses at permissions, and a log nobody else can read. As agents multiply, so does the sprawl. Payments needed a network. Data needed a lake. Actions need a lake too.
How it works
An action lake has six parts:
- A catalogue of declared actions, each with an input schema, an effect class (read, write, destructive) and a canonical name.
- A vault that holds vendor credentials apart from agents. See credential vault.
- Identity and policy: a key per agent with permissions, caps and approvals.
- A meter in a unit that tracks real cost: the upstream call.
- Joins and a cache, so questions across systems are answered in one call and repeated reads are not.
- A ledger that records every action with its reason. See hash-chained ledger.
Example
Three teams build three agents: one in an IDE, one in a chat app, one in a scheduled job. All three connect to the same action lake. Finance can see what each spent, security can see what each did, and revoking one agent's key stops only that agent.
Common pitfalls
What an action lake is not:
- Not a copy of your data. Reads go to the vendor or a short-lived cache; it is not a warehouse.
- Not a flow builder. Nobody drags boxes. Agents and schedules start the work.
- Not a model. It works with whichever model your agents use.
How actionlake handles it
In practice, actionlake is an action lake delivered as one MCP gateway: eight tools, 1,000+ connectors and 289,000+ actions (counted 24 October 2026), a vault sealed per tenant, scoped keys, caps refused before dispatch, approvals, a tenant cache where cached answers are free, ask for cross-system answers, and a hash-chained ledger. Request and response traces are kept only for your plan's window. Start on the platform page, or connect a client in the quickstart.
Questions
Is an action lake the same as a data lake?
No. A data lake stores data for analysis. An action lake governs actions: what agents may do in other systems, what they did, and what it cost. It does not keep a copy of your business data.
Is an action lake the same as an MCP gateway?
An MCP gateway is one way to deliver it. The action lake is the whole layer: catalogue, vault, policy, meter, joins and ledger, reachable over MCP or REST.
Does an action lake replace my automation platform?
It changes how work starts: agents and schedules instead of hand-built flows. It has no event triggers or webhooks by design, so check whether your processes need them.
Related terms
- MCP gatewayAn MCP gateway is a single MCP endpoint that sits between AI agents and many systems, holding the credentials and enforcing permissions, limits and logging on every tool call that passes through it.
- Upstream callAn upstream call is one HTTP request that actionlake sent to a vendor's API on your behalf and that returned a definitive, attributable response; it is the unit actionlake meters and bills.
- Canonical verbA canonical verb is one standard name for the same operation across every system that supports it, written domain.object.operation, such as service.ticket.create, so an agent learns an action once and can run it against any connected system.
- Tamper-evident ledgerA tamper-evident ledger is an append-only record in which each entry stores a cryptographic hash of its own contents together with the previous entry's hash, so any later edit, deletion or reordering breaks every hash after it and becomes detectable.
- Credential vaultA credential vault for AI agents stores the API keys and OAuth tokens for business systems encrypted outside the agent, and uses them on the agent's behalf only at the moment of each call, so the model, its prompt and its logs never contain a vendor secret.
- Cross-system joinA cross-system join combines records from two or more separate systems that refer to the same real-world thing, such as one customer in an accounting ledger and a ticket desk, and returns a single combined answer.
Sources
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