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Make Decagon act across your systems

Decagon is great at automating customer support conversations at scale. Decagon resolves conversations, but the action that closes the loop — refund, update, fulfillment — lives in systems of record it must reach safely. Upware is the harness that closes that gap.

Governed by policy & RBAC Full audit trail Outbound-only on-prem bridge No rip-and-replace
The wall

Decagon can decide. It can't safely do.

Decagon is sharp at the hard part — reading a conversation, understanding what the customer needs, and deciding what should happen next. But deciding and doing are two different things, and the systems that actually own refunds, account updates, and fulfillment records don't belong to Decagon. Bolting on direct API access sounds like the obvious fix until you're living with what it creates: brittle point-to-point connections that break on schema changes, writes that carry no attribution, no approval layer, and no audit trail — and an agent that has effectively unbounded access to production systems it was never designed to govern.

Brittle integration glue
Every system gets its own hand-built connector that breaks on the next change.
No governance
API keys and service accounts hand the agent more power than policy allows.
Unattributable actions
Writes land with no actor, no reason, and no audit trail.
Unbounded action
An open-ended agent acting directly — with no policy or verification around it — can take a wrong, irreversible action.
The harness

AI to learn. Deterministic to execute.

Upware learns the process once and encodes it as a mostly-deterministic workflow: system interactions replay exactly, and any LLM steps are wrapped in policy, verification, and audit — so execution stays governed and drift-proof.

O
Observe & Learn
Records the real workflow once, across every system it touches.
G
Generate
Builds the API, data flow, and logic automatically — no integration project.
E
Encapsulate
Wraps it as a governed AI skill over secure MCP or API.
R
Run
Executes deterministically — RBAC, audit, and scale built in.
FAQ

Common questions

Does Upware replace Decagon, or does it work alongside it?
Upware doesn't touch Decagon's core job. Decagon still runs the conversation, reads the context, and decides what action is needed. Upware sits between that decision and the systems of record that need to act on it — handling the integration, enforcing policy, and writing a full audit trail. The two products divide the problem cleanly: Decagon owns resolution intelligence, Upware owns governed execution.
How does Decagon actually call an action through Upware?
Upware exposes each learned workflow as a governed AI skill over MCP or API. Decagon calls it like any other tool — no custom integration work required on Decagon's side. On Upware's side, that call triggers deterministic replay of the recorded workflow: the exact UI events, web requests, or system interactions that a human would have performed, wrapped in RBAC and policy checks before anything is written.
What prevents Decagon from doing something it shouldn't — a wrong refund amount, an action on the wrong account?
Upware applies policy and verification rules at the execution layer, before any action reaches a system of record. RBAC controls which skills Decagon can invoke and under what conditions. Each execution is logged to an immutable audit trail, so every write is attributed, timestamped, and reviewable. Upware does use AI in bounded ways — for learning workflows and for certain runtime checks — but the actual system interactions replay deterministically, which means the same inputs produce the same outputs every time, without the variance you'd get from an unconstrained LLM deciding how to interact with a live system.

Give Decagon a safe way to act

See how Upware turns your agent's decisions into governed, audited execution.

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