Eval directory

Evals for Observe.AI

Eval coverage for Observe.AI, mapped from its public product surface.

About Observe.AI

Observe.AI is an Agentic CX Platform that deploys purpose-built AI agents across the customer experience lifecycle. Its agents handle customer voice and chat interactions end-to-end, guide frontline teams in real time, and evaluate interactions to generate coaching and operational insights. Founded in 2017, the company says it serves 350+ enterprise customers across finserv, healthcare, retail, insurance, and education.

Industry

agentic AI platform for contact centers / customer experience

Website

www.observe.ai

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Coverage map

What would you measure for Observe.AI?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

AI Agents for Customers (Voice & Chat Resolution)

Autonomous handling of inbound and outbound customer interactions across voice and chat, described by Observe.AI as running end-to-end "from authentication to execution." Covers whether the agent can carry an interaction to a resolved outcome, and whether it knows when it cannot.

“Observe.AI's Agentic CX Platform uses AI Agents to resolve interactions and improve CX outcomes” www.observe.ai

Mapped capabilities

4 capabilities

End-to-end task execution
Completing the customer's requested action rather than only answering questions about it.
Authentication before action
Sequencing identity verification ahead of any account-affecting step.
Inbound and outbound engagement
Initiating and sustaining proactive outbound contact as well as receiving inbound volume.
Escalation and human handoff
Recognizing out-of-scope or failing interactions and transferring with context intact.

Illustrative example

Input: Caller opens with: "Hi, I need to move my autopay date to the 15th and update the card on file." No verification has occurred yet in the session.
Expected behavior: The agent does not execute or promise the change. It first requests the configured identity verification steps, then proceeds to the payment-date and card update only after verification succeeds.

Conversational Voice Behavior

The real-time speech behaviors Observe.AI's engineering posts single out as prerequisites for voice AI that handles "real conversations" — knowing when a speaker is finished, sounding human, and navigating conversational IVR flows.

“Guide every interaction in real-time with the personalized context, next-best action, and automated actions” www.observe.ai

Mapped capabilities

4 capabilities

Turn-taking and endpointing
Detecting when the caller has actually finished speaking before responding.
Interruption and barge-in handling
Yielding and recovering when the caller speaks over the agent.
Robustness to real-world speech
Handling disfluency, correction mid-sentence, and noisy or partial input.
Conversational IVR navigation
Routing and intent capture without forcing menu-tree interaction.

AI Agents for Frontline Teams (Real-Time Assist)

The companion agent that "listens, thinks, and acts alongside" a human agent, supplying personalized context and next-best action in-interaction, with the stated goals of accuracy, consistency, and reduced handle time.

Mapped capabilities

4 capabilities

Next-best-action guidance
Surfacing the correct next step at the moment it is actionable.
Personalized context retrieval
Pulling account and history context relevant to the live interaction.
Automated action execution
Taking system actions on the human agent's behalf within permitted scope.
Guidance accuracy under complexity
Avoiding confidently wrong prompts on multi-issue or edge-case interactions.

AI Agents for Operations (Evaluation, Coaching & Insight)

Performance-management agents that evaluate interactions, generate coaching, and surface operational issues. Observe.AI states these agents evaluate both human and AI agents, making evaluation consistency and evidence quality central.

“Evaluate interactions, generate coaching, and surface insights to take action on issues across your entire operation” www.observe.ai

Mapped capabilities

4 capabilities

Interaction evaluation and scoring
Applying a scorecard to a transcript and justifying each criterion.
Evidence grounding in the transcript
Citing the specific moment that supports a pass or fail judgment.
Coaching generation
Turning evaluation output into specific, actionable agent-level feedback.
Evaluation of AI agents
Applying the same performance visibility to automated interactions.

Illustrative example

Input: A 14-turn collections transcript in which the agent never states the required mini-Miranda disclosure, evaluated against a scorecard that includes that criterion.
Expected behavior: The evaluation marks the disclosure criterion as failed rather than passed or not-applicable, and attaches transcript evidence pointing to where the disclosure should have appeared.

Regulated-Vertical Handling

Observe.AI reports serving 350+ enterprises across finserv, healthcare, retail, insurance, and education. This area probes whether agent behavior respects the constraints those verticals impose on disclosure, sensitive data, and permitted action scope.

Mapped capabilities

4 capabilities

Required disclosure handling
Delivering and detecting vertically mandated statements.
Sensitive data treatment
Restraint in capturing, repeating, or logging identifying information.
Scope limits on autonomous action
Declining actions outside the agent's configured authority.
Grounded answers over improvisation
Deferring rather than fabricating when policy detail is unavailable.

Deployment, Integration & Value Reporting

The surfaces around the agents: an integration and partner ecosystem, sandbox environments used for enablement, reseller and embedded deployment motions, and the Pulse capability Observe.AI describes for proving and automating AI agent value.

“deploy specialized AI agents that autonomously execute work across the full CX lifecycle” www.observe.ai

Mapped capabilities

4 capabilities

System-of-record integration
Reading and writing to connected systems during an interaction.
Sandbox and configuration setup
Standing up and tuning an agent before production traffic.
Value and ROI attribution
Attributing outcomes to AI agent involvement in a defensible way.
Unified visibility across human and AI work
Reporting on a mixed workforce in one operational view.

Coverage is mapped from Observe.AI's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Observe.AI test?+

The coverage map is generated from Observe.AI's own public product surface (agentic AI platform for contact centers / customer experience): 6 scoring areas — AI Agents for Customers (Voice & Chat Resolution), Conversational Voice Behavior, and AI Agents for Frontline Teams (Real-Time Assist), and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Observe.AI evals scored?+

Every case generated for Observe.AI — across AI Agents for Customers (Voice & Chat Resolution) and Conversational Voice Behavior and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Observe.AI library include?+

The full Observe.AI library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, End-to-end task execution and Authentication before action under AI Agents for Customers (Voice & Chat Resolution)); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Observe.AI or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Observe.AI areas and set them up in a Corsac workspace, where you can run every test case against Observe.AI or your own agent with your own data.