One platform for building, running, and governing AI agents in production.
Each agent is scoped to one job, runs against your data, and operates under an approval gate you control. Runtime, policy, and audit — handled. Your team reviews what matters.
Each agent scoped to one job. In your environment, against your rules.
Each agent is configured around a single goal, with the tools, data, and guardrails scoped to that job. We build with you, against your systems.
- Goal-scoped — one agent per job.
- Tool allowlist — only the systems and APIs the agent needs.
- Guardrails per agent — what it can read, write, and trigger.
- Configurable approval mode — auto, review, or strict.
Nothing leaves the queue without you. Every output reviewed before any action is taken.
Every agent has an approval queue. Drafts land there first. The agent never sends, writes, or triggers directly. Your team reviews, edits, and approves.
- Per-agent approval mode — auto, review, or strict.
- Editable drafts — your team can change anything before it goes out.
- Bulk actions — approve similar items together.
- Notifications on every pending item.
- Pending review
Lead reply · Acme Co.
agent drafted · awaiting your review
- Pending review
Reorder · SKU-4129
agent drafted · awaiting your review
- Approved
Support reply · order #8841
you approved 2m ago
Rules enforced before generation. Not a filter at the end. A gate at the start.
The policy stack runs before the draft — not after. Voice, claims, policy, confidentiality, brand are checked at the policy layer.
- Three scopes — organizational, agent-specific, execution-time.
- Per-agent severity — pass, warn, block.
- Fails closed — if the policy layer is down, the agent does not run.
- Versioned policies — every change logged and reversible.
Voice · claim · brand · confidentiality
No outbound to non-customer domains
Output must reference current order status
Every action logged. Append-only. Reconstructable months later.
Two synced logs capture every input, output, and decision. Append-only, queryable — the answer to what the agent did, and why, is in the trail.
- Append-only — nothing rewritten, nothing deleted.
- Two syncs — internal and exportable.
- Filterable by agent, actor, action, time.
- Reconstructable — rebuild any run from inputs and outputs.
| Time | Actor | Action | Context |
|---|---|---|---|
| 04:21:08 | agent | draft.created | Customer reply · #8841 |
| 04:21:09 | policy | voice.check.pass | — |
| 04:21:09 | policy | claim.check.pass | — |
| 04:21:11 | user | draft.approved | sara@ |
| 04:18:42 | agent | draft.created | Customer reply · #8839 |
| 04:18:43 | system | send.completed | via gmail.send |
| 04:16:09 | agent | kb.search | ‘refund policy’ |
Connected to where the work already happens. Email, CRM, support, internal APIs.
Agents plug into the systems your team already uses. We do the wiring — OAuth, scopes, rate limits, retries. The audit log captures every call.
- Email — Gmail, Outlook, SES, Postmark.
- CRM — Salesforce, HubSpot, Pipedrive.
- Support — Zendesk, Intercom, Front, Help Scout.
- Internal — REST and GraphQL, auth handled by the platform.
We run it. You review it. Runtime and monitoring, ours.
We handle the runtime — model selection, retries, monitoring, configuration drift — so the agents stay sharp after launch. You see a report and a queue.
- Runtime — model selection, retries, timeouts, fallbacks.
- Monitoring — health, latency, cost, error rate per agent.
- Weekly reports — what the agents did, where they needed you.
- Configuration updates — policies, tools, prompts tuned monthly.
- Customer reply · draft created
- Lead routing · draft approved
- Customer reply · draft created
- Lead routing · draft approved
- Customer reply · send completed
See what the agent did, and where it needed you. Outputs, approvals, where judgment was applied.
Reports show what the agent ran, how often your team approved without changes, and where judgment was applied. The trend, not just the count.
- Outputs over time — volume, mix, change rate.
- Approval rate — approved as-is vs. edits.
- Where judgment was applied — which drafts needed edits and why.
- Trend — week over week, month over month.
Edit rate trending down · fewer edits as the model improves
See how the platform fits your business.
A walkthrough using your context, your tools, your data.