Observe.AI

Unifies AI agents and interaction intelligence to resolve customer requests, guide frontline teams, and improve CX performance.

Safely test, release, and measure agent changes so teams can improve production AI while maintaining control.

Helps CX and operations teams turn governed procedures into testable AI agent drafts in plain English, without requiring engineering to author agent logic.

Turns customer conversations into shared, actionable intelligence to improve quality, understand customer journeys, and guide decisions across teams and AI agents.

Connects business systems and runs resilient workflows so agents can use real-time context and complete work across tools.

Agentic CX Platform

Observe.AI’s Agentic CX Platform brings together AI agents for customers, frontline teams, and operations. It supports the full agent lifecycle: building, orchestrating, governing, evaluating, and optimizing agents across voice and digital conversations. Agents connect to enterprise systems to complete workflows, while shared interaction intelligence provides context across the platform. Built-in policies, guardrails, testing, monitoring, and controls support deployment and ongoing improvement. Customer agents resolve requests across voice, chat, web, app, SMS, and social messaging; Companion Agents guide employees before, during, and after interactions; and operations agents analyze performance, provide coaching, and uncover customer insights.

Category: Customer Support Automation Software

• Build And Improve AI Agents
  Manage agents across their lifecycle, from building and testing to deployment, monitoring, and optimization.
• Coordinate Agents Across CX
  Connect customer-facing automation, frontline assistance, and operational improvement in a unified system with shared context.
• Resolve Customer Requests Across Channels
  Complete customer service work across voice and digital channels, including actions in connected business systems.
• Complete Actions In Connected Systems
  Enable agents to use existing business systems and workflows to take action and complete customer service tasks.
• Evaluate Interactions And Deliver Coaching
  Automate quality evaluation and provide evidence-backed coaching to help teams monitor performance and improve operations.
• Handle Real-World Calls Naturally
  Support responsive voice conversations that account for caller interruptions, turn-taking, background noise, and specialized vocabulary.
• Reach Customers Across Digital Channels
  Offer AI support on websites and connect it to messaging channels customers already use.
• Guide Frontline Teams In Real Time
  Give employees customer context, knowledge, process guidance, compliance prompts, coaching, and action recommendations throughout interactions.
• Prepare Agents With Customer Context
  Help agents understand who is contacting them, why they may be calling, and what happened previously.
• Prompt Required Actions During Interactions
  Help agents follow applicable procedures and address compliance requirements while the conversation is in progress.
• Transfer Conversations With Full Context
  Route unresolved or higher-risk interactions to people while preserving conversation history and customer context.
• Answer From Approved Knowledge
  Help agents provide responses based on authorized information rather than unsupported content.
• Protect Sensitive Interaction Data
  Support secure caller workflows and mask sensitive information in transcripts and actions while enforcing required call steps.
• Surface Emerging Customer Signals
  Monitor conversations for emerging issues and trends that teams can investigate.
• Investigate Questions In Plain Language
  Explore customer experience data by starting with a question or hypothesis and following signals as an investigation develops.
• Verify Findings And Explain Root Causes
  Understand the scale and drivers of customer issues through quantified findings grounded in interaction evidence.
• Govern Agents With Defined Controls
  Set agent permissions and policies, control releases, and roll back changes to support accountable deployment.
• Test Agents Before And After Deployment
  Assess agent behavior and outcomes through simulations, regression tests, and scoring to inform release decisions.
• Share Interaction Context Across Agents
  Use interaction signals as shared memory to inform agent actions and support continuity across the customer experience.
• Capture Details And Trigger Workflows
  Reduce manual work by extracting interaction details, preparing form fields, and initiating relevant actions in connected systems.
• Recover From Unresolved Interactions
  Provide a safe path when an agent cannot resolve a request or a connected system encounters a problem.
• Tailor Coaching To Team Needs
  Focus coaching on specific skills and apply consistent standards and preferred frameworks across teams.
• Reinforce And Measure Coaching
  Reinforce relevant practices during conversations and monitor performance changes over time.
• Connect Customer Insights To Business Actions
  Help teams use customer conversation insights to inform cross-functional decisions about products, processes, and business priorities.
• Monitor Performance And Surface Improvements
  Give teams visibility into live activity and performance trends, with analysis and recommendations to support operational action.
• Operate Within Enterprise Controls
  Support deployment in environments requiring security, privacy, compliance, and visibility into agent performance and actions.
• Document Interaction Outcomes
  Create summaries and record dispositions to document completed conversations and next steps.
• Tailor And Enrich Chat Experiences
  Customize the chat interface and use interactive responses to support customer conversations.

Agent Harness

Agent Harness is an AI agent lifecycle management platform that helps operations teams manage agents like critical software. It provides isolated workspaces for safely editing agent logic, version history and change review, simulated conversation testing, release gates, phased traffic rollouts, and one-click rollback. Teams can track KPIs by agent version and link interactions to the version that handled them, making it possible to evaluate changes and trace anomalies. Agent Harness also supports governed knowledge updates, including active syncing from source systems and conflict resolution to keep agents aligned with approved information.

• Isolate Changes From Production
  Make and review agent changes in a separate workspace without affecting the version handling live interactions. Full history and side-by-side diffs help teams understand what changed before promoting an update.
• Roll Out Changes Gradually
  Start with a portion of live traffic, expand when results support it, and use one-click rollback if a change causes problems.
• Gate Releases On Reviews And Tests
  Require review and successful evaluation before an agent update reaches customers, helping teams check changes and address regressions before release.
• Test Agent Changes Before Release
  Run simulated conversations against a workspace version to evaluate agent behavior before launch, including scenarios that go beyond the happy path.
• Trace Interactions To Agent Versions
  Connect interaction outcomes and anomalies to the precise version and change involved, helping teams understand the impact of releases.
• Compare Performance Across Versions
  Review relevant KPIs side by side across agent versions to assess the impact of an update before committing to full deployment.
• Keep Agent Knowledge Current
  Sync updated policies from source systems and resolve conflicting definitions through governance, helping agents use current, approved information.

CoBuilder

CoBuilder is an AI agent configuration tool that turns policies, procedures, and governed context into complete, working agent drafts. Teams describe what they want an agent to do in plain English; CoBuilder maps the conversation flow, selects actions, and asks clarifying questions when needed. It can use connected systems and available tools, then creates a draft in an isolated Safe Workspace for review and testing. Drafts remain separate from live traffic until they pass the required checks and receive reviewer approval.

Category: AI Agent Platforms

• Turn Existing Procedures Into Agent Inputs
  CoBuilder uses existing policies, procedures, and knowledge sources as the basis for agent configuration, helping teams work from the materials that already define their processes.
• Draft Agents Through Plain-English Collaboration
  CX leads, QA managers, and operations teams can author and refine agents without engineering help or node editors. CoBuilder uses their instructions to draft an agent and asks for clarification rather than guessing when needed.
• Review And Test Drafts Before Release
  CoBuilder output remains an isolated workspace draft rather than going live automatically. Teams can review and test it through the Agent Harness lifecycle, and approved changes are required before a version touches real customers.
• Map Process Logic Automatically
  CoBuilder translates documented procedures into agent logic, including decision branches, fallbacks, and edge cases, reducing the need to construct those flows manually.
• Connect Agent Drafts To Governed Tools
  CoBuilder is aware of the connected stack and configures agents to use available systems and tools, avoiding manual tool configuration and integration tickets. Tool calls follow the access controls defined by the team.
• Build Required Compliance Checks Into Drafts
  CoBuilder incorporates compliance checkpoints from source procedures and QA rubrics into agent drafts, helping teams account for required process steps as they configure agents.

Interaction Intelligence

Observe.AI Interaction Intelligence analyzes customer conversations across voice and digital channels, scoring quality, extracting structured signals, and stitching interactions into a continuous customer journey. It provides shared, governed intelligence for AI agents, QA teams, and business leaders. The platform supports automated evaluation against quality rubrics, evidence-linked scoring, manual review and calibration, cross-channel case management, and transcription with sensitive-data redaction.

• Evaluate Every Interaction
  Apply quality criteria consistently across every call and chat, with supporting conversation evidence linked to each score.
• Unify Customer Journeys
  Give teams and agents a connected view of interactions across channels so they can understand the full issue rather than isolated conversation snippets.
• Extract Conversation Signals
  Make customer and operational information in conversations available as structured signals for analysis and agent use.
• Share Context Across Agents
  Help connected AI agents ground their answers and actions in shared customer memory, while giving teams a common source of interaction intelligence.
• Transcribe And Redact Data
  Make conversation content usable as transcripts while masking PII, PCI, and PHI before model processing.
• Trace Scores To Evidence
  Help teams inspect the conversation moments behind scores and metrics, supporting review and auditability.
• Calibrate Human And AI Scores
  Support review of nuanced or high-stakes interactions and help teams align AI scoring with their standards.
• Discover Emerging Patterns
  Surface recurring customer and operational signals across interactions so teams can investigate issues and identify opportunities for improvement.

Integration Fabric

Integration Fabric connects Observe.AI agents and operations to business systems through more than 250 prebuilt connectors, open APIs, and Model Context Protocol support. Its workflows retrieve live context, run multi-step jobs across connected systems, and continue through slowdowns or outages. Workflows can execute alongside the language model, resume from saved state, and be reused across agents. The platform also supports data synchronization, logging, permissions, auditability, and enterprise data protections.

• Complete And Resume Multi-Step Jobs
  Run jobs across connected systems from beginning to end, with durable state that allows long-running work to resume and continue after a conversation closes.
• Connect Agents To 250+ Business Systems
  Use prebuilt connectors to link AI agents and operations with systems already used across the business.
• Ground Agent Responses In Live Data
  Retrieve relevant customer and account information in real time so agents can use grounded facts when responding.
• Recover Workflows From System Failures
  Use retries, timeouts, and fallback paths to handle transient failures and keep multi-step jobs from ending in dead ends.
• Connect Custom Systems With Open APIs And MCP
  Extend connectivity to internal and third-party systems through open APIs and Model Context Protocol support, with governance for those connections.
• Keep Conversations Moving During Backend Work
  Run asynchronous and long-running steps alongside the model so conversations do not stall while backend work completes.
• Enrich And Sync Conversation Data
  Combine conversation data with CRM context and metadata, then synchronize reporting, summaries, and sentiment scores to connected business platforms.
• Track And Govern Every Workflow Step
  Use logs, permissions, auditability, and data-access controls to maintain visibility into workflow execution.
• Protect Data Across Connected Systems
  Apply encryption, PII redaction, and access controls to help protect data as it moves through integrations.