Autonomous agents
Agents run a full reasoning loop — plan, call tools, observe results, decide the next step — rather than answering once. Each run is bounded by iteration, tool-call and time limits you set.
AI capabilities
Agents that act on your business — and the governance to let them
A chatbot answers questions. An agent does the work: reads the record, searches the knowledge base, triggers the workflow, and leaves an audit trail behind it.
Tool calling with permissions
Agents act through a registry of typed tools, and every tool call is checked against the agent's allowlist and the acting user's own permissions. An agent can never exceed the person it runs for.
Grounded retrieval
Answers are grounded in your documents through hybrid semantic and keyword search with graph traversal, so responses cite your material instead of inventing it.
Long-term memory
Conversations are scored and summarised so relevant history is carried into later runs. Memory is selected by relevance, not simply appended until the context window fills.
Multi-agent orchestration
A coordinating agent delegates to specialists and merges their results, with configurable depth, parallelism and total agent count so a run cannot fan out indefinitely.
Provider-agnostic
Anthropic, OpenAI and Google behind one interface. Bring your own keys, route different workloads to different models, and switch provider without touching your agents.
Streaming by default
Responses stream token by token over SSE with heartbeats and resumable event ids, so a slow tool call or a dropped connection doesn't lose the run.
Human approval gates
Mutating actions can require explicit sign-off. The run pauses, waits for a decision — for days if needed — then resumes exactly where it stopped.
Complete run history
Every step, tool call, token count and cost is recorded per run and per tenant. You can answer what an agent did, why, and what it spent.
Governed by default
The reason agents can be trusted with real operations is that the constraints are enforced below them, not requested of them.
- Tenant isolation enforced in the ORM layer, independently of application code
- Per-organization AI credentials, so your keys and spend stay yours
- Usage and cost metered per organization and per feature
- No customer data is ever used to train foundation models
More detail on how we handle security and data.
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