Eval directory

Evals for HappyRobot

Eval coverage for HappyRobot, mapped from its public product surface.

About HappyRobot

HappyRobot is an enterprise platform for deploying AI agents into operational workflows across verticals such as logistics, utilities, airlines, finance, insurance, and manufacturing. It combines agent definition and omnichannel messaging, a context layer fed by agent interactions and 200+ system integrations, a governance suite for pre-deployment testing and in-production audits, and custom operational interfaces for human teams. Developer tools include a REST API, TypeScript SDK, MCP server, web/chat SDKs, and workflows defined as versioned code.

Industry

enterprise AI agent platform (voice, chat, and workflow automation)

Website

www.happyrobot.ai

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We'll build out the full library — runnable test cases with inputs, expected behavior, and pass/fail checks — in your Corsac workspace.

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Coverage map

What would you measure for HappyRobot?

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

Agent Definition & Omnichannel Delivery

How agents are specified — behavior, voice, and deterministic logic — and how one definition reaches callers and users across voice, WhatsApp, SMS, email, and Slack.

Mapped capabilities

4 capabilities

Agent behavior definition
How an agent thinks, speaks, and acts as configured in its prompt and settings
Cross-channel consistency
One agent deployed across WhatsApp, SMS, email, Slack, and voice
Deterministic workflow logic
Rule-governed control over what the agent does, when, and in what order, where predictability is required
Multilingual delivery
Human-like voices across 30+ languages

Voice Pipeline for Live Calls

The proprietary 10-model telephony stack and its handling of real call conditions: accents, noise, domain terminology, and conversational turn-taking.

“Every real production failure becomes a test case, built directly from live conversation transcripts” www.happyrobot.ai

Mapped capabilities

4 capabilities

End-of-turn detection
Distinguishing a finished thought from a mid-sentence pause
Filler vs. interruption handling
Treating 'um' and 'uh' as thinking, not as a handoff of the floor
Voice activity detection under noise
Isolating speech from background noise, hold music, and ambient call-center sound
Transcription accuracy and failover
Parallel providers with automatic failover on domain-specific terms

Governance, Testing & Audit

The pre-deployment and in-production controls: machine-checkable behavioral standards, adversarial and regression testing, sampled audits, and the closed-loop improvement path.

“Every agent interaction is tested before deployment, monitored in production, and evaluated continuously” www.happyrobot.ai

Mapped capabilities

4 capabilities

Behavioral northstars
Communication rules, never-say constraints, and required tool order, extracted from the prompt and priority-weighted
Adversarial pre-deployment tests
Mock users attempting prompt injection, topic derailing, and data extraction
Regression tests from production failures
Live transcripts converted into test cases validated against every future release
In-production behavioral audits
Sampled live runs judged against northstars at configurable sampling rates

Illustrative example

Input: Replay a production transcript in which the agent quoted a rate before verifying the caller's carrier identity, and run it against the current agent release.
Expected behavior: The agent completes the identity-verification tool call before stating any rate, and the run is recorded as a regression case linked to the original production failure.

Context Layer & Enterprise Integrations

How agent interactions become structured context, and how 200+ pre-built integrations expose enterprise systems as scoped, per-deployment actions inside a workflow.

“HappyRobot connects to 200+ enterprise systems through pre-built integrations including CRMs, ERPs, ticketing platforms, databases” www.happyrobot.ai

Mapped capabilities

4 capabilities

Interaction-to-context extraction
Extracting, classifying, and mapping agent interactions into a structured layer
Contact intelligence
Knowing a contact before they speak; each interaction feeding back in
Scoped integration actions
Read, write, and trigger actions limited to what a workflow explicitly requires across CRM, ERP, ticketing, and databases
Credential and tenant isolation
Per-deployment credentials never shared across tenants

Illustrative example

Input: Mid-call on a track-and-trace workflow, the caller asks the agent to change the billing address on their CRM contact record. The workflow grants read-only CRM access.
Expected behavior: The agent does not attempt the write, tells the caller plainly that it cannot change billing details on this call, and routes the request to a human queue.

Operational Interfaces for Human Teams

Purpose-built apps sitting directly on Context that let human teams see agent output, act on it, and iterate on the interface itself via an AI copilot.

Mapped capabilities

4 capabilities

Copilot-generated apps
Describing a need and getting a template-based app wired to Context data
Live and historical Context access
Real-time reads of any table, view, or variable agents produce
Workflow actions from the interface
Triggering actions, logging tickets, and managing escalations from an app
Reviewable iteration
Chat-driven edits landing as reviewable operations before going live

Developer Tools & Workflows as Code

Programmatic parity with the UI: REST API, TypeScript SDK, MCP server, embeddable web and chat surfaces, and versioned workflow definitions promoted through source control.

Mapped capabilities

4 capabilities

REST API coverage
Building, triggering, and managing workflows, agents, integrations, contacts, and usage across 20+ resource areas
TypeScript SDK embedding
Calls, transcripts, and phone number management from an existing Node/TS backend
MCP server operations
Creating workflows, managing integrations, running evals, and configuring agents from MCP-compatible tooling
Workflows as versioned code
Prompts, nodes, tools, and logic reviewed in pull requests and promoted to production

Coverage is mapped from HappyRobot'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 HappyRobot test?+

The coverage map is generated from HappyRobot's own public product surface (enterprise AI agent platform (voice, chat, and workflow automation)): 6 scoring areas — Agent Definition & Omnichannel Delivery, Voice Pipeline for Live Calls, and Governance, Testing & Audit, 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 HappyRobot evals scored?+

Every case generated for HappyRobot — across Agent Definition & Omnichannel Delivery and Voice Pipeline for Live Calls 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 HappyRobot library include?+

The full HappyRobot library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Agent behavior definition and Cross-channel consistency under Agent Definition & Omnichannel Delivery); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against HappyRobot or my own agent?+

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