Agents, Deployments & Channels
Build an agent once, publish it as a deployment to a defined audience, and reach it over web chat, API, browser voice, phone, and messaging. Improve the agent once and every placement improves.
Governed Agent Operations
From memory to action.
Specialist agent teams built around real operations (with scoped expertise, connected tools, guardrails, handoffs, and approval paths), not generic assistants with loose access.
Generic agents with broad access do not survive enterprise operations.
An agent that can answer anything is not safe to act on anything. Enterprise work needs specialists: one agent for intake, another for research, another for drafting, another for approval, each with the right context, the right tools, and clear authority.
Governed Agent Operations builds agent teams around the operation, not around the prompt, and governs them twice: at runtime, where every tool call, handoff, and reply runs under policy, and at change time, where every configuration change is proposed, reviewed, and applied by a human. The invariant behind the whole module: software proposes, a human applies.
Inside Governed Agent Operations
How Governed Agent Operations works
Six steps decompose an operation into a safe, observable agent team.
Design the agent team
Decompose the business operation (quote review, claims handling, customer onboarding) into the agent roles that mirror how a real team handles it.
Bind expertise and context
Connect each agent to the expertise packages and Company Expertise context it needs, and only what it needs.
Define action boundaries
Set what each agent can read, reason about, draft, execute under policy, or escalate for human approval.
Connect tools and permissions
Give each agent narrow, auditable access from a catalog of 180+ integrations: read tools by default, write tools behind admin enablement and runtime approval, never a generic toolbox.
Configure handoffs
Define when work passes from one agent to another, to a human, or into an approval queue, with the conditions explicit. Every handover carries full context, so nothing is re-asked.
Deploy and observe
Publish the team to its audience over the channels it needs (workspace, embedded widget, API, browser voice, phone) and track execution, guardrail firings, handoffs, and approvals in production.
What a governed operation contains
Everything an agent team needs, and nothing it should not have.
- Operation design & process flow
- Agent team & role configuration
- Expertise & context bindings
- Deployments, audiences & channels
- Scoped tool permissions & approval modes
- Intent, topic & condition routing
- Guardrail & condition library
- Human handover & live support
- Change requests, commit log & rollback
- Execution audit & performance metrics
Capabilities
Six engines keep agent teams specialised, observable, and safe.
Routing & Decisioning
Intents, topics, and reusable conditions route conversations explicitly. There is deliberately no hidden confidence threshold: every path is inspectable and testable.
Tool Governance
One funnel for 180+ integrations, MCP servers, and platform tools: read/write tiers resolved server-side, runtime approval for writes, standing rules, and an append-only Tool Audit.
Guardrails
Reusable safety and processing rules (prompt injection detection, moderation, PII redaction, code and LLM policies) bound at six placements from input to error path. Every firing is recorded.
Human Handover & Live Support
Escalation to staff with the full transcript and context, silent monitoring, transfers, and an Agent Panel workspace. Handover changes who talks; tool approval governs what the AI may do.
Governed Change Control
Change requests, playground testing, environments, evals with golden scenarios, and an append-only Commit Log. Rollback is itself a proposal; there is no side door back in time.
Why this is different
Built on expertise, not generic prompts
Agents inherit structured judgment from mined expertise, not broad instructions you hope they interpret correctly.
Specialist teams, not one assistant
Multiple agents with one responsibility each, with clear handoffs, the way real teams work.
Observable and auditable
Every decision, tool call, approval, and handoff is logged with evidence; compliance reads it, learning systems use it.
Software proposes, a human applies
AI may observe, analyze, draft, and propose, but a person applies every live configuration change, at every level of autonomy. There is deliberately no auto-apply.
See Verti run on your operations.
Verti connects expert judgment, live context, governed action, and learning loops into one production system.