Autonomous AI Lifecycle Management

Manage AI agents like critical software

Agent Harness gives your operations teams the safety nets of enterprise change management — version control, automated testing, and phased rollouts — wrapped in workflows built for the people closest to the customer.

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You wouldn't push changes straight to a live IVR or CRM workflow. Agents deserve the same discipline. Safe Workspaces give builders a fully isolated copy of an agent to experiment in — with zero impact on the version answering real calls.

Key Capabilities:

Edit safely. Prompt logic, policy rules, workflow steps, fallbackbehavior, and knowledge references — all without touching production.

See every change side by side. Track exactly how a tweak altersintent handling, routing, and escalation before it ships.

Role-based approvals. Promote updates through reviewworkflows so every adjustment is checked for compliance and accuracy first.

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Approved changes don't have to be all-or-nothing launches. Release from a specific workspace to a sliver of live traffic, watch the real-world numbers, and expand only when the data earns it. If something slips, recovery is a single click.

Key Capabilities:

Phased traffic. Test a new auth step or routing rule on a smallpercentage before expanding across the contact center.

Documented releases. Tag every release with a description andattach evaluation results to prove operational readiness.

Version-level traceability. Every interaction is tied to the versionthat handled it — trace any anomaly to the exact release,workspace, and change that caused it.

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Dumping PDFs into a basic RAG system retrieves text from ungoverned data — stale numbers, conflicting definitions, content a user shouldn't see. The Context Center resolves definitions, lineage, and access policies before retrieval, keeping every agent synced to a single source of truth.

Key Capabilities:

Active knowledge syncing. Event-driven sync with sourcesystems — when a human updates a policy, the agent has it immediately. No stale uploads.

Human guidelines to agent logic. Translates ambiguous SOPsinto explicit, machine-executable routing trees and fallback behavior.

Conflict resolution & governance. Marketing says 30 days, legalsays 14? The Context Center enforces one certified answer.

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Approved changes don't have to be all-or-nothing launches. Release from a specific workspace to a sliver of live traffic, watch the real-world numbers, and expand only when the data earns it. If something slips, recovery is a single click.

Key Capabilities:

Phased traffic. Test a new auth step or routing rule on a smallpercentage before expanding across the contact center.

Documented releases. Tag every release with a description andattach evaluation results to prove operational readiness.

Version-level traceability. Every interaction is tied to the versionthat handled it — trace any anomaly to the exact release,workspace, and change that caused it.

Where Agents do the work —and  remember it

Two shared layers sit beneath every phase: the fabric that lets agents act inside your systems, and the intelligence layer that turns every interaction into shared memory.

Action & Integration Fabric

Where agents reach into your stack to complete the work — not just talk about it.
crm
CCaaS
Knowledge bases
Open APIs & MCP
Telephony
Workflow automation

Customer Interaction Intelligence

Unified signals extracted from every interaction — the shared memory layer across all agents.
Transcripts
Intents
Sentiment
Outcomes
QA signals
Journey memory

Entreprise Trust & Governance

Security, privacy, and compliance are built into the platform — not bolted on. Every agent runs inside the controls your enterprise already requires.

One platform for the entire AI agent lifecycle

See how Observe.AI builds, orchestrates, governs, evaluates, and optimizes AI agents across every customer conversation.