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An AI agent is grounded only when its answer is supported by the evidence it was given or retrieved. A plausible sentence is not proof. The reliable way to test grounding is to control the evidence, include questions the evidence cannot answer, add contradictions and counterfactuals, and then test memory across multiple sessions with separate retrieval and answer scores.
What “grounded” means in an AI agent
Grounding is an evidence relationship, not a tone of confidence. An answer is grounded when its factual claims are entailed by the supplied context or by a memory item the agent legitimately retrieved. If the context says a project launched on 14 March, a grounded answer can state that date and point to the supporting passage. If the context is silent, the correct behavior is to say that the information is unavailable rather than fill the gap with a likely-sounding detail.
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This distinction matters because language models are optimized to produce coherent continuations. They can answer a familiar question correctly from prior training even when the supplied document says something different, or invent a detail when no supporting passage exists. A polished response therefore measures fluency; it does not by itself demonstrate evidence use.
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Grounding, retrieval and memory are different operations
- Grounding: the final claims follow the supplied or retrieved evidence.
- Retrieval: the system finds and presents the memory or document passage needed for the answer.
- Memory: the system stores information, preserves its provenance and applies later updates across sessions.
- Reasoning: the system combines relevant facts correctly instead of merely copying a matching sentence.
An agent can retrieve the right paragraph and still reason incorrectly. It can also produce the right answer for the wrong reason, such as relying on prior world knowledge while ignoring a contradictory context window. A real evaluation scores each operation separately.
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Build a controlled evidence pack before testing
Start with a frozen set of short documents or memory records. Give every fact a stable identifier, date and provenance so that a reviewer can verify exactly what the agent saw.
What to put in each record
- A unique fact ID, such as
F-017. - The fact in unambiguous language, including units, dates and named entities.
- The source or speaker and the time at which the fact became valid.
- Optional distractor facts that are topically similar but irrelevant.
- A version label for the evidence pack, such as
pack-2026-01.
Do not change this pack while a test run is in progress. A frozen pack lets you reproduce an error and compare two agents without silently changing the task. Keep the prompt, model version, context limit, retrieval settings, memory policy and evaluation split fixed as well.
Require an evidence pointer
Ask the agent for a concise answer plus the fact IDs or quoted spans that support it. Evidence pointers are not proof that the model used those passages—the model can cite an irrelevant sentence—but they make unsupported claims easier to detect. A reviewer should check each claim against the cited text rather than awarding points for citation formatting.
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A test made only of ordinary answerable questions rewards guessing. Include four deliberately different conditions so that the agent has to demonstrate when it should answer, abstain or override prior knowledge.
1. Answerable context
Give the agent a document that explicitly contains the answer. Ask a direct question and require a short response with an evidence pointer. Score whether the answer is correct, complete and supported by the stated passage. Include both easy fact lookups and questions requiring two facts from the same document.
Example: A project brief states that the release is scheduled for 14 March 2026 and supports 12 languages. Ask, “When is the release, and how many languages are supported?” A fully grounded answer must preserve both values; mentioning only the date is incomplete.
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2. Unanswerable context
Ask about a detail that is absent from the supplied material. The expected behavior is an explicit abstention, such as “The provided context does not state the budget.” Do not give credit merely because the model avoids a random number: it should identify the information gap and avoid implying that an outside source was consulted.
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3. Inconsistent context
Include two statements that conflict, such as an earlier record saying a plan costs $40 and a later record saying the price changed to $45. Tell the agent how to handle conflicts—or leave that policy to the test—and score whether it identifies the inconsistency rather than blending the values.
For a time-aware system, the expected answer may be the newer value together with a note that an older record differs. For a system without temporal rules, the safe answer is to surface both claims and ask for clarification. The key measurement is whether the model follows the supplied records instead of silently selecting the value it has seen most often elsewhere.
4. Counterfactual context
Replace a familiar real-world fact with an invented one. For example, a fictional product brief can state that its battery lasts 73 hours. Ask about battery life while the context is present. A grounded agent should answer 73 hours even if general knowledge suggests a different range. Counterfactual cases reveal whether the system actually conditions on the prompt.
Keep the fictional status clear to the evaluator, but do not make the text so absurd that the model can reject it as nonsense. Use realistic names, dates and quantities, and include a few irrelevant facts to prevent keyword matching.
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Score evidence use separately from answer quality
Use a claim-level rubric instead of a single “looks good” rating. Break each response into factual claims, then evaluate support, correctness and behavior when support is missing.
| Dimension | What to measure | Typical failure |
|---|---|---|
| Evidence support | Proportion of answer claims entailed by the supplied or retrieved context. | A correct-sounding detail has no supporting passage. |
| Answer correctness | Whether the final answer reaches the right conclusion for the task. | The needed passage was retrieved, but arithmetic or reasoning is wrong. |
| Retrieval recall | Whether the required memory or passage appeared in the agent’s working context. | The answer fails because the relevant record never surfaced. |
| Retrieval precision | How much of the retrieved material is relevant rather than distracting. | A small relevant fact is buried under a large pile of unrelated text. |
| Abstention | Whether the agent declines unsupported questions and labels uncertainty. | It invents a date, source or explanation for an absent fact. |
| Contradiction handling | Whether conflicting records are detected and resolved according to policy. | Old and new values are merged into a value that appears nowhere. |
| Temporal consistency | Whether a later valid update overrides an older value when appropriate. | The agent keeps repeating a superseded preference. |
| Cost and latency | Tokens, retrieval calls and judge calls needed per answer. | A high score depends on an impractical number of calls. |
Report scores by condition, not only as one average. An agent that scores 90% on answerable questions but abstains correctly only 20% of the time has a different risk profile from one with lower recall but excellent refusal behavior.
Test memory across sessions and time
A single long prompt is not a long-term memory test. Multi-session evaluation checks whether facts survive conversation boundaries, whether distributed information can be combined, and whether updates take effect.
Information extraction
During an initial conversation, provide several user facts—such as a preferred measurement unit, a project deadline and a dietary restriction. Test whether the memory system extracts the right items and preserves their provenance. Do not score storage alone: an item can be written correctly but never retrieved later.
Multi-session reasoning
Place complementary facts in separate sessions. In session one, establish a client’s region; in session two, establish the tax rule; in session three, ask for the final price calculation. The answer requires combining records that were never adjacent in the prompt. Record which memories were retrieved and whether the final calculation used both.
Temporal updates
Change one fact deliberately. A user might first say, “My delivery address is Oak Street,” then later say, “I moved to Pine Street.” Ask about the address after the update and also ask what it was before the move. A robust system distinguishes current state from historical state instead of returning whichever value was stored first.
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Abstention from forgotten or ambiguous facts
Ask about a detail that was never stored, was explicitly deleted, or has two unresolved values. The agent should say that it does not know or that the records conflict. Treat a confident guess as a memory failure even when the guessed answer happens to be true.
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What established benchmarks can and cannot tell you
Benchmarks are useful when their task design matches your risk. Always record the dataset version, prompt format, context budget, memory policy, judge model and evaluation split before comparing scores.
| Benchmark | Focus | Useful detail | Interpretation caution |
|---|---|---|---|
| FACTS Grounding | Long-form answers grounded in a supplied document. | Google DeepMind reported 1,719 examples covering summarization, question answering and rewriting. The benchmark uses Gemini 1.5 Pro, GPT-4o and Claude 3.5 Sonnet as frontier-model judges, with held-out human-rater checks. | It measures grounding against supplied documents, not every form of persistent memory. |
| FACTS Benchmark Suite, Grounding Benchmark v2 | Grounding in prompt context with realistic retrieval and reasoning. | The December 9, 2025 update includes examples requiring multiple sequential web-retrieval steps. | Retrieval and reasoning can be entangled in these tasks, so inspect both when diagnosing an error. |
| FaithEval | Contextual faithfulness under difficult evidence conditions. | The ICLR 2025 benchmark contains about 4.9K problems covering unanswerable, inconsistent and counterfactual context, with multi-stage construction, LLM evaluation and human validation. | A score is meaningful only with the stated split and judging procedure. |
| FRAMES | Retrieval-augmented generation across factuality, retrieval and reasoning. | It is designed to expose cases where a relevant passage is retrieved but the answer still reasons incorrectly, or is correct for the wrong reason. | Do not collapse its three dimensions into a single retrieval number. |
| LongMemEval | Long-term conversational memory. | Five abilities are tested across 500 questions embedded in scalable chat histories: extraction, multi-session reasoning, temporal reasoning, updates and abstention. | Results depend on history length, memory implementation and the exact evaluation setup. |
| LoCoMo | Very long-term conversational recall. | The ACL 2024 benchmark is useful for extended conversations and multiple memory categories. | Implementations and leaderboards can change; report dataset version, categories, judge and context setup. |
| HaluMem | Operation-level hallucination diagnosis for memory systems. | The MemTensor repository covers memory extraction, update, retrieval, response generation and end-to-end memory question answering. | Use operation-level results to locate where an error entered rather than blaming final generation automatically. |
FACTS Grounding describes its purpose as measuring how accurately language models ground responses in source material and avoid hallucinations. The suite’s later update illustrates why a realistic agent test may require several retrieval steps, while FaithEval demonstrates that unanswerable, inconsistent and counterfactual contexts must be treated as first-class cases. FRAMES is the clearest reminder that retrieval success and reasoning success are separate events.
A repeatable evaluation procedure
- Freeze the environment. Record model name and version, system and user prompts, context limit, temperature or sampling settings, retrieval index, memory policy, judge, dataset version and split.
- Load the evidence pack. Assign every fact a stable ID, date and provenance. Keep the exact text available for audit.
- Run answerable questions. Require concise answers and evidence pointers. Score claim support and correctness separately.
- Run unanswerable questions. Include near-neighbor details and mark unsupported completion as a failure.
- Run contradiction cases. Present conflicting statements and apply a documented policy for conflict resolution or clarification.
- Run counterfactual cases. Use realistic fictional facts that conflict with common prior knowledge.
- Run multi-session cases. Split related facts across sessions, then ask for a combined answer.
- Apply updates and deletions. Change or remove a fact, then test current-state, historical and abstention questions.
- Log retrieval separately. Save the passages or memory IDs returned before generation, along with rank, score and token count when available.
- Audit a sample by hand. Have reviewers check whether judge labels agree with the evidence. Automated judges can be wrong, so report the judging model and any human validation.
- Publish the complete setup. Include prompts, context limits, memory rules, dataset version, split, judge and cost or latency measurements with every score.
Diagnose failures by where they entered
The fact was never stored
If extraction output omits a user statement, later retrieval cannot recover it. Inspect the write step, entity and date parsing, deduplication rules and deletion handling. Add extraction tests before changing the generator prompt.
The fact was stored but not retrieved
Check query formation, embedding or keyword matching, filters, recency weighting and the number of retrieved items. A memory can be present in the database yet absent from the model’s context. This is a retrieval-recall failure, not necessarily a reasoning failure.
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The right passage was retrieved but ignored
Compare the retrieved text with the final claims. If the agent cites a passage but contradicts it, test shorter contexts, clearer conflict instructions and evidence-required output. Keep the retrieval result fixed while changing generation settings so the two effects are not mixed.
The answer is correct for the wrong reason
Use counterfactual facts and ask for claim-level pointers. If changing the supplied fact does not change the answer, the system may be relying on prior knowledge or a learned pattern. A correct answer in an ordinary case should not receive full grounding credit when it fails the counterfactual version.
The judge is inconsistent
Run a held-out set with human review, define entailment and abstention criteria in advance, and retain the exact judge prompt. Different judges can disagree about partial support, especially in long-form answers. Never compare two systems using different judges or different context windows.
How to compare two agents fairly
Use the same frozen evidence, question order, prompts where possible, context budget, retrieval limits, memory policy, judge and split. Randomize presentation order if one agent could be affected by session sequence. Report a table with at least evidence support, answer correctness, retrieval recall, retrieval precision, abstention, temporal consistency, multi-session reasoning, cost and latency.
Explain the denominator for every percentage. “Abstention accuracy” might count only unanswerable questions, while “overall accuracy” may include answerable, contradictory and counterfactual cases. Include the number of questions and confidence intervals or reviewer agreement when available. A benchmark score is evidence about a defined task distribution, not proof that an agent never guesses.
Common traps that make a test look better than it is
- Using only answerable questions: the model can guess and still score well.
- Treating a large context window as memory: capacity does not show that the right item was stored, retrieved or updated.
- Counting citations as grounding: an irrelevant citation can accompany an unsupported claim.
- Mixing retrieval and generation scores: a missing passage and a bad inference require different fixes.
- Changing the judge between runs: score differences may reflect the evaluator rather than the agent.
- Ignoring time: a stale fact may look correct if the test has no update cases.
- Reporting a leaderboard number without setup details: prompts, subsets, context limits and scoring rules can make nominally similar numbers incomparable.
- Testing only one conversation length: memory failures often appear when facts are separated across sustained interactions.
A compact test card for each release
For every agent version, retain a one-page record containing:
- Model and release date.
- System prompt, user prompt template and sampling settings.
- Evidence-pack and benchmark versions.
- Context limit and retrieved-item limit.
- Memory write, update, deletion and expiry rules.
- Scores for answerable, unanswerable, inconsistent and counterfactual cases.
- Retrieval recall and precision, answer correctness and claim-level support.
- Multi-session, temporal-update and abstention results.
- Judge model, human-validation method and evaluation split.
- Average tokens, retrieval calls, judge calls and latency.
Keeping this card with the test artifacts makes regressions visible. If a new prompt improves answerable accuracy but reduces abstention, the trade-off is explicit rather than hidden in one blended score.
The practical verdict
To tell whether an AI agent used context, make the context decisive: include facts it must use, facts it cannot know, facts that contradict prior knowledge and facts that change over time. Verify what was retrieved, inspect whether each claim is supported, and measure memory across sessions rather than inferring reliability from a long context window or a confident tone. FACTS Grounding, FaithEval, FRAMES, LongMemEval, LoCoMo and HaluMem provide complementary starting points, but your own frozen evidence pack and transparent scoring rules are what make a result relevant to the agent you operate.
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