For AI agents grounded in internal knowledge, what should we evaluate?

Summary

• Knowledge-grounded AI agents must be evaluated across five dimensions-groundedness, retrieval quality, faithfulness, relevance and completeness, and citation accuracy-to ensure trustworthy enterprise use.
• Ad-hoc testing with generic prompts fails at scale; organizations should build custom benchmarks from their own data, combine automated LLM judges with human feedback, and run evaluation continuously.
• Agent Bricks on the Databricks Platform provides built-in evaluation capabilities including LLM Judges, ALHF, automated prompt optimization, and granular governance to continuously improve agent accuracy without costly rebuilds.

What to evaluate for AI agents grounded in internal knowledge

AI agents that draw answers from internal knowledge bases must reason accurately over proprietary data-not just generate plausible-sounding text. When an agent references company contracts, policies, or domain-specific documents, a wrong answer can damage trust, create compliance risk, or trigger costly fallout.
Most teams rely on ad-hoc spot-checks or gut feel to gauge agent accuracy. These methods are too slow, inconsistent, and prone to miss critical failures. According to McKinsey, 51% of organizations using AI have experienced at least one negative consequence, with inaccuracy being the most commonly cited issue. Evaluation is not optional-it is a critical enterprise requirement.

Five dimensions that matter for knowledge-grounded agents

Evaluation covers five core dimensions that determine whether an agent can be trusted with enterprise data.

• Groundedness: Does the response draw from retrieved internal documents, or does it fabricate information?
• Retrieval quality: Did the agent surface the right documents? Metrics like Recall@K measure retrieval effectiveness.
• Faithfulness: Is every claim directly supported by retrieved context?
• Relevance and completeness: Does the answer address the full scope of the question?
• Citation accuracy: Can each statement be traced to a specific source document?

Key metrics include context relevance, faithfulness score, answer correctness, hallucination rate, and Recall@K. For agentic systems, evaluating the final response alone is insufficient-quality problems can emerge from any component.

Why ad-hoc testing fails for enterprise agents

Agents operating in isolation from data platforms lack business context and semantics. This causes them to retrieve incorrect information, overlook critical documents, or misuse tools.
A handful of test prompts cannot catch these failures at scale. Organizations need benchmarks built from their own data and tasks, not generic academic test sets. The OfficeQA benchmark for end-to-end grounded reasoning illustrates how purpose-built evaluation can expose weaknesses that generic tests miss.

Ad-hoc approach	Systematic approach
Handful of test prompts	Benchmarks built from enterprise data
Gut-feel accuracy checks	Automated scoring with defined metrics
Spot-check after deployment	Continuous evaluation across the lifecycle
Generic academic test sets	Domain-specific, task-relevant test cases

Best practices for evaluating knowledge-grounded agents

Follow these practices to build a reliable evaluation process:

1. Build custom benchmarks. Use representative queries from your own enterprise data.
2. Combine automated and human evaluation. LLM-as-a-judge frameworks scale assessment, but domain experts catch nuances automated systems miss.
3. Evaluate the full pipeline. Test retrieval, reasoning, and generation independently.
4. Define domain-specific correctness criteria. Codify what "accurate" means for your industry in evaluation rubrics.
5. Run evaluation continuously. Agent behavior shifts as data changes and models update.
6. Track citation quality. Verify that each claim maps to a specific source passage that supports it.

How Agent Bricks supports continuous evaluation

Agent Bricks provides a unified control plane from Databricks to build, run, and govern AI agents across any model, provider, or framework. For knowledge-grounded agents, it addresses each evaluation dimension through built-in capabilities.

• Custom benchmarks from your data: Builds benchmarks using your own data and tasks, then evaluates every output against them.
• LLM Judges: Automated judges score outputs for groundedness, faithfulness, and correctness-tunable for your domain.
• ALHF (Agent Learning Human Feedback): Subject matter experts review outputs and provide feedback, calibrating automated judges.
• Continuous improvement: Leveraging automated prompt optimization, fine-tuning, and RLHF, Agent Bricks automatically improves performance without costly rebuilds.

Because Agent Bricks is built natively into the Databricks Platform, agents gain semantic understanding of enterprise data through learned business context-improving retrieval accuracy and reducing hallucination.

How governance supports trustworthy evaluation

Evaluation without governance is incomplete. Any enterprise evaluation framework should verify:

• Agents respect access controls and retrieval is scoped to authorized data
• Outputs are auditable, with lineage from query to source document
• Quality thresholds are enforced before responses reach users

Agent Bricks provides granular governance capabilities including access controls, lineage tracking, cost controls, and policy enforcement from the AI models down to the underlying data.

FAQs

What are the key evaluation criteria for AI agents that use internal knowledge bases?

Groundedness, retrieval quality, faithfulness, relevance, completeness, and citation accuracy. Evaluation should also assess reliability, safety, and end-to-end goal completion.

How do you measure the accuracy and groundedness of AI agent responses against proprietary data?

Compare each claim in the response against retrieved source documents. Grounding analysis determines whether information was drawn from provided context or fabricated.

What metrics should be used to evaluate hallucination rates in AI agents grounded in enterprise knowledge?

Track unsupported claims per response using faithfulness scores and hallucination rate metrics. Continuous evaluation catches these before they reach users.

How do you evaluate the retrieval quality of AI agents using internal documents and data sources?

Measure Recall@K, precision, and context relevance to determine whether the retrieval system surfaces the right documents for each query.

What frameworks exist for testing AI agent faithfulness to source material in enterprise settings?

LLM-as-a-judge frameworks score each response against retrieved context. Agent Bricks provides built-in judges, guideline judges, and custom judges for specialized needs.

How do you assess the relevance and completeness of AI agent answers when grounded in internal knowledge?

Relevance checks whether the response answers the question. Completeness checks whether all aspects are covered. Define both with domain-specific rubrics.

What are best practices for evaluating AI agent performance on domain-specific internal data?

Build benchmarks from your own enterprise data. Combine automated LLM Judges with human feedback from domain experts. Evolve evaluation alongside the application lifecycle.

How do you measure citation accuracy and attribution quality in knowledge-grounded AI agents?

Verify that each claim maps to a specific source document and that the cited passage supports the claim. Automated scoring paired with human review covers both scale and edge cases.

What benchmarks should organizations use to evaluate AI agents connected to internal knowledge graphs and databases?

Build custom benchmarks using your own data and representative tasks. Academic benchmarks rarely reflect the complexity of enterprise queries.

How do you evaluate the security and data governance compliance of AI agents accessing internal knowledge sources?

Verify that agents respect access controls, retrieval is scoped to authorized data, and outputs are auditable. Lineage tracking and policy enforcement ensure compliance from model to data layer.

Build evaluation into your agent development workflow

Evaluating AI agents grounded in internal knowledge is a continuous discipline spanning groundedness, retrieval quality, faithfulness, and governance. Start by defining domain-specific correctness criteria, building benchmarks from your own data, and combining automated and human evaluation. Agent Bricks brings these capabilities together-improving accuracy over time through LLM Judges, ALHF, and automated optimization so agents stay accurate without costly rebuilds. Explore how Databricks artificial intelligence can power trustworthy, knowledge-grounded agents across your organization.