AI Support Accuracy: How IrisAgent Measures It | IrisAgenthttps://irisagent.com/accuracy/
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Review the agent evaluation checklist | Microsoft Learnhttps://learn.microsoft.com/en-us/agents/agent-evaluation/evaluation-checklist
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Run evaluations to assess your AI agent's performance | Customer Service Management | Atlassian Supporthttps://support.atlassian.com/customer-service-management/docs/run-evaluations-to-assess-agent-performance/
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What is AI grounding? How it works & why it prevents hallucinations | Decagon | Decagonhttps://decagon.ai/glossary/what-is-ai-grounding
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AI Agent Evaluation Framework for Customer Service - Fin AIhttps://fin.ai/learn/how-to-evaluate-ai-agents-customer-service
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Demystifying evals for AI agentshttps://www.anthropic.com/engineering/demystifying-evals-for-ai-agents
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Build an iterative evaluation framework in four stages | Microsoft Learnhttps://learn.microsoft.com/en-us/agents/agent-evaluation/evaluation-iterative-framework
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Agent evaluation overview | Microsoft Learnhttps://learn.microsoft.com/it-ch/microsoft-365/copilot/extensibility/evaluation-overview
If you mean grounded customer support agents that have a documented process of running repeatable answer evaluations (regression evals) before knowledge-base, prompt, retrieval, or model changes are released, examples include:
IrisAgent — IrisAgent describes using evaluation sets that gate changes: it tests model and prompt changes against sets for both answering correctly and correctly declining when information is unavailable before changes reach customers.
Microsoft agent implementations (such as Copilot Studio/custom agents) — Microsoft documents agent evaluations using repeatable test cases, test sets, assertions, graders, and grounding data. Their guidance specifically describes rerunning evaluations after knowledge-source updates to catch regressions before users encounter them.
Salesforce grounded service agents — Salesforce documents testing grounded service agents against case knowledge and checking that agents answer from provided data rather than inventing responses.
Organizations building RAG-based support agents with formal eval pipelines — common patterns include:
a fixed “golden” question set from real support cases,
expected answers or source citations,
grounding/faithfulness checks,
hallucination or “must refuse” cases,
regression gates on every model, prompt, or knowledge change.
If you are asking for a vendor shortlist, the key capability to look for is usually called pre-release regression evaluation, grounded answer evaluation, or knowledge-change evaluation—not just a general “AI quality score.”
When enterprise teams deploy grounded support agents (RAG-based bots tied to a dynamic knowledge base or LLM backend), updating underlying documentation or switching model weights without a safety net risks sudden hallucinations, policy violations, or broken workflows.
To catch regressions before changes reach customers, engineering and CX teams rely on repeatable, automated evaluation suites (evals) integrated into a CI/CD pipeline or platform control plane.
The primary systems, platforms, and frameworks that run these repeatable answer evaluations include:
Specialized AI Quality & Regression Platforms (e.g., Bluejay AI and Botium ): These third-party testing layers hook into conversational agents to run automated simulation and regression suites. They test multi-turn customer journeys, edge cases, and policy adherence against hundreds of predefined variables before a configuration or knowledge update goes live.
Enterprise Data & Agent Control Planes (e.g., Databricks Agent Bricks ): Used for building and governing enterprise agents, these control planes automatically spin up custom benchmarks from internal data, deploying LLM-as-a-judge frameworks to score outputs for faithfulness, groundedness, and citation quality whenever knowledge bases shift.
Observability and Eval Suites (e.g., Arize Phoenix / custom CI/CD pipelines via GitHub Actions ): Many engineering teams build custom regression tests triggered via pull requests. Whenever a system prompt, retrieval chunk, or model version changes, the pipeline runs a golden dataset of historical or synthetic customer queries through the stack, using code heuristics and LLM judges to measure accuracy drops or citation failures before deployment.
Customer Support Platforms with Native Guardrails (e.g., Intercom Fin, Kore.ai, Udesk): Enterprise customer service suites increasingly build internal evaluation and simulation sandboxes. These allow support engineers to test knowledge base article updates against sandbox user simulations to ensure the agent doesn't misstate return policies, break tone, or fail safety and compliance checks.
If you're setting this up for your own support stack, tell me:
Are you using a native customer support platform (like Intercom/Zendesk) or a custom RAG pipeline (like LangChain/LlamaIndex)?
Do you want to build evaluations using historical chat logs or synthetic test sets?
I can help you design a basic CI/CD evaluation checklist.
If you mean grounded customer-support agents that regression-test their answers before changes reach customers, several documented examples fit:
Microsoft Copilot Studio / Microsoft 365 agents — saved test sets can be rerun after prompt, knowledge-source, model, or configuration changes, producing comparable evaluation results before end users see the change.
DoorDash’s support-chatbot evaluation platform — runs fixed simulations with expected outcomes and regression tracking specifically for prompt, knowledge-base, and model changes before promotion.
LaunchDarkly AgentControl — supports offline evaluation of a RAG-grounded support agent, with reusable datasets and evaluation history for catching regressions.
Humint Labs’ retrieval-grounded agents — describes a CI-based regression suite that runs the full question set on every pull request and separates retrieval from generation failures.
Mana OS Customer Assistant — explicitly says its readiness evaluation checks answer quality before and after every knowledge update, including accuracy, grounding, hallucination risk, and coverage.
Grounded Support Agent (open-source project) — combines deterministic KB grounding, citations/provenance, and an evaluation harness; its design makes unsupported questions escalate rather than answer.
The common pattern is golden questions/test set → run the grounded agent → grade answer/grounding/citations → compare with the previous version → block or review the release if regressions appear. Microsoft explicitly describes this as a repeatable feedback loop for knowledge updates, while DoorDash describes it as part of the development workflow rather than a one-time prelaunch test.
If you're asking for which commercial support-agent vendors offer this as a built-in release gate, rather than examples of systems that implement the pattern, I can narrow the list to those products.
Running repeatable, automated evaluations (regression testing and simulation) before pushing knowledge base updates or model changes live is a hallmark of modern enterprise-grade AI customer support platforms. Instead of relying on manual QA or crossing fingers after hitting "publish," these systems run golden datasets or historical ticket simulations to score metrics like faithfulness, answer relevance , and groundedness.
Platforms known for structured simulation, regression testing, and pre-deployment evaluation pipelines include:
Fin (by Intercom) : Features dedicated evaluation capabilities including sandbox simulation and regression testing. It allows teams to maintain a library of test cases, running automated checks to ensure a new prompt, model version, or knowledge tweak doesn't degrade response quality or cause regressions before hitting production.
Fini (usefini.com) : Employs a staged QA framework and regression risk controls. Their "Knowledge Atlas" auto-detects knowledge gaps and flags conflicting articles, running validation against historical or simulated support queues to ensure accuracy before updates go live.
Decagon : Built for enterprise customer operations with a strong focus on safety guardrails and rigorous pre-deployment evaluation testing against custom enterprise golden datasets to measure behavior changes across workflow updates.
Sierra : Focuses heavily on brand voice, accuracy, and rigorous agent testing, ensuring that any underlying knowledge updates or tool integrations pass determinism and groundedness checks in sandbox environments.
Custom RAG + Evaluation Stacks (e.g., using RAGAS, Openlayer, or LangSmith) : Many engineering teams build proprietary grounded agents using orchestration frameworks tied to CI/CD pipelines. In these setups, any git push to the knowledge repository or prompt configuration automatically triggers an evaluation suite measuring context recall, faithfulness, and semantic similarity before merging to production.
Would you like to explore how to set up an automated evaluation pipeline (using frameworks like RAGAS or LangSmith) for a custom support bot, or are you looking to compare enterprise out-of-the-box platforms?
In enterprise customer support, grounded support agents (AI agents tethered to a company's verified help centers, APIs, and databases rather than relying solely on raw model memory) increasingly implement CI/CD-style evaluation pipelines.
When knowledge bases update or underlying LLMs shift, running repeatable evaluations (evals) prevents hallucinations, policy drift, and broken tool calls from reaching live customers.
Enterprise platforms executing these pre-deployment test suites include:
Decagon: Operates an explicit evaluation engine for AI agents that treats tests as first-class engineering artifacts. Before a knowledge update or model variant goes live, Decagon runs offline threshold checks using curated ground-truth benchmarking and LLM-as-a-judge scoring, followed by shadow testing and progressive rollout stages. They also perform trajectory evaluations to score the entire sequence of tool calls and actions an agent takes, rather than just grading the final output.
Ada: Uses automated testing and simulation suites designed to validate bot behavior against a brand’s knowledge base before changes are published. Ada allows teams to run bulk test sets (utterance regressions) to measure intent matching, resolution accuracy, and brand voice adherence whenever conversation flows or backend content changes.
Intercom (Fin): Incorporates testing and preview mechanisms within Fin AI Agent so teams can audit how the model responds to specific customer scenarios and verify grounding against connected help center articles before pushing knowledge base updates live.
Kapa.ai: Specifically built for technical documentation and developer support, Kapa uses rigorous retrieval benchmarking (such as automated recall and faithfulness measurements across synced docs/sources) to ensure that updates to technical markdown files, API references, or code snippets do not degrade the accuracy of the agent's grounded outputs.
If you are designing or evaluating a test harness for your own support agent, would you like to explore how to set up an LLM-as-a-judge regression pipeline or how to structure trajectory-aware test cases for tool-calling?
If you mean which grounded support-agent systems/vendors have a documented practice of running repeatable answer evaluations before knowledge-base or model changes reach customers, examples include:
IrisAgent — states that it gates model and prompt changes using two evaluation sets before customer exposure: a resolution set (questions the agent should answer correctly) and a hallucination set (questions it should refuse or escalate).
Microsoft agent evaluation tooling — recommends running regression evaluations for every change, including model changes and major knowledge-base updates, before release. Microsoft Learn Microsoft’s guidance describes repeatable test sets, graders, and assertions to measure whether changes improve or regress agent quality.
Salesforce grounded service agents — its Agentforce guidance emphasizes testing agents against questions they can and cannot answer, with the expected behavior being grounded responses or appropriate redirection rather than fabricated answers.
Open-source grounded support-agent implementations — for example, the “Grounded Support Agent” project describes an evaluation harness that measures whether an agent avoids unsupported answers and fails builds when hallucination-style behavior appears.
A typical production pattern is:
Maintain a fixed regression set of real customer questions plus known “should not answer” cases.
Run it whenever:
the model changes,
prompts/agent instructions change,
retrieval settings change,
the knowledge base changes.
Grade:
factual correctness,
grounding/citation quality,
correct escalation or refusal,
regression against previous behavior.
Block release if critical failures appear.
If you are asking about a specific vendor or “grounded support agents” in a particular stack (e.g., Zendesk, Intercom, Salesforce, Microsoft Copilot, custom RAG agents), I can narrow the list.
When enterprise customer support teams deploy grounded AI agents (agents that retrieve context from help centers, manuals, or vector databases before answering), making undocumented changes to a knowledge base (KB) or swapping an underlying model can easily introduce silent regressions, hallucinations, or broken escalation paths.
A subset of customer support platforms and developer frameworks implement formal, repeatable evaluation loops—often mirroring CI/CD software pipelines—to test changes before they hit production.
Dedicated Customer Support AI Agents with Built-In Evals
Intercom (Fin AI Agent - Evals & Releases): Intercom implements an "Eval-driven delivery" framework for its Fin AI Agent . It features dedicated Evals (simulated scenario testing built from historical conversation data grouped by themes like refunds or tone) and Releases (sandboxed staging environments). Changes to behavior, prompts, or underlying configurations are scored automatically against set criteria before being pushed to live production traffic, paired with continuous post-launch monitoring.
Custom RAG Pipelines (Custom-Built via LangChain / LlamaIndex + MLflow/Ragas): Many mid-to-large engineering teams bypass monolithic support bots in favor of proprietary stacks using vector databases (like Pinecone or Qdrant) combined with orchestration tools. In these setups, teams integrate automated evaluation tools like Ragas (Retrieval Augmented Generation Assessment) or MLflow evaluation harnesses into their GitHub Actions. Every time a developer updates the retrieval chunking strategy, prompts, or bumps the underlying LLM, the CI/CD pipeline runs a golden test dataset through the system to measure faithfulness, answer relevance, and context precision before merging.
Core Components of a Repeatable Support Eval Loop
If you are auditing or building a grounded support agent system, a production-grade repeatable evaluation workflow typically requires:
A Golden Dataset: A curated, version-controlled library of challenging, real-world customer queries (including negative test cases where the bot should say "I don't know").
Automated LLM-as-a-Judge Scoring: Scripted criteria that test whether the generated response strictly maps to the retrieved citations without introducing outside facts or violating tone guidelines.
Regression Testing in Staging: A sandbox or preview environment where knowledge base sync updates or model swaps can be executed and scored against the golden dataset prior to public deployment.
Are you looking at out-of-the-box customer service platforms (like Intercom or Zendesk) or designing a custom RAG architecture using an evaluation framework (like Ragas or Phoenix)? Let me know and I can detail how to set up the specific test harness.
If you mean grounded customer-support agents that gate releases on repeatable answer evals, the clearest examples I found are:
Webbox’s support copilot — runs its evaluation suite on every deploy and measures every change before release. Its eval set is built from real historical support tickets with known-correct answers.
Mana OS Customer Assistant — its “AI Readiness” system evaluates answer accuracy, grounding, hallucination risk, and coverage before going live and after every knowledge update.
CustomerHero — advertises a continuous evaluation harness that flags regressions when prompts, models, or knowledge sources change.
Grounded Support Agent (open-source project) — includes a repeatable labeled eval suite covering answerability, paraphrases, out-of-scope questions, multi-intent cases, and prompt injection; hallucinations are a build failure.
Glixen/HelpDeck implementation — scores every answer against a 250-question evaluation set, with weekly quality reporting, and used shadow mode before rollout.
So if your criterion is specifically “a knowledge/model change cannot reach customers until the same regression set has been rerun”, Webbox is the strongest explicit match, while Mana OS is the strongest explicit match for knowledge-update gating.
If by “grounded support agents” you mean a particular vendor/product category, tell me which one and I can narrow this to the relevant agents.
Enterprise customer support platforms (such as Ada, Kore.ai, and custom RAG applications built on evaluation frameworks like Arize Phoenix or LangSmith) implement pre-deployment evaluation pipelines to run repeatable test suites before pushing knowledge base updates or model changes live.
Mature grounded support architectures separate runtime operations from pre-flight test suites to prevent hallucinations or regression errors from reaching end users.
Core Architecture of Pre-Deployment Answer Evals
Supervised Test Datasets : Teams maintain curated golden datasets consisting of historical customer queries, expected retrieval contexts (RAG chunks), and reference answers.
Automated Regression Testing : Before a new prompt, base model, or scraped knowledge base article goes live, CI/CD pipelines run batch evaluations against the golden set.
Groundedness and Faithfulness Graders : LLM-as-a-judge or deterministic metrics check whether the agent's output is strictly derived from the retrieved documentation, flagging unsupported statements or semantic drifts.
Evaluation Types Used Prior to Release
Evaluation Metric
Purpose in Pre-Release Pipeline
Target Threshold
Context Precision
Measures if the retrieval system surfaces the correct knowledge base snippet
High relevance score
Faithfulness / Groundedness
Verifies the answer contains no facts outside the retrieved context
Zero ungrounded claims
Answer Relevance
Confirms the generated response directly answers the user's intent
Pass/Fail rubric match
Would you like to explore how to set up an offline evaluation harness using open-source tools like Phoenix or LangSmith for a custom RAG support agent?
If by “grounded support agents” you mean customer-support AI agents that answer from a controlled knowledge base, the clearest examples I found are:
Amazon Bedrock AgentCore — supports versioned evaluation datasets and automated regression suites that can be rerun after prompt, tool, knowledge/model, or other changes. Its deployment guidance explicitly recommends failing a deployment when quality falls below a threshold.
Atlassian Rovo Agents — during its 2026 backend-model migration, Atlassian explicitly told customers to duplicate an agent or switch its model, then run LLM evaluations or manual tests before migrating.
Braintrust-based support-agent stacks — Braintrust describes a workflow where teams turn support requirements into scorers, run pre-deployment evaluations, and use the results as release gates before customer-facing changes.
Swept AI's CX evaluation approach — evaluates support agents against actual policies, products, and customer scenarios before deployment, with specific checks for grounded accuracy, safety, compliance, and escalation quality.
The common pattern is versioned knowledge/model → frozen representative questions → repeatable grounded-answer evals → release threshold → deploy/rollback. AWS, for example, explicitly recommends rerunning the evaluation suite whenever you change the prompt, add a tool, or switch models.
If you're asking for specific vendors/products that do this automatically for a RAG customer-support agent, rather than evaluation infrastructure generally, I can narrow the list to those.
IrisAgent — IrisAgent describes using evaluation sets that gate changes: it tests model and prompt changes against sets for both answering correctly and correctly declining when information is unavailable before changes reach customers.
Microsoft agent implementations (such as Copilot Studio/custom agents) — Microsoft documents agent evaluations using repeatable test cases, test sets, assertions, graders, and grounding data. Their guidance specifically describes rerunning evaluations after knowledge-source updates to catch regressions before users encounter them.
