A context layer reduces hallucinations through four controls, each stopping a different class of error. Grounding stops invented definitions and formulas. Constraint keeps the agent off deprecated sources and, once access policy is enforced where the query runs, off rows the user should never see. Verification traces a cited field to an authoritative source where lineage exists. Abstention turns a missing, conflicting or stale signal into a decline. No control stops a model that misreads correct context. For causes, see why AI agents hallucinate and the types and causes of LLM hallucinations.
Atlan exposes the signals behind those controls to your agent at query time over a hosted MCP server: glossary terms mapped to assets, certificate status, owners, freshness timestamps, a data quality verdict on search results, lineage, and metadata scoped to the requester’s Atlan permissions (Atlan MCP tools; Atlan MCP asset metadata). A context layer for AI agents turns context engineering into checks an agent runs before it answers.
| Question | Short answer |
|---|---|
| What a context layer controls | What the agent may treat as true: definitions, sources, scope, provenance, freshness |
| The four controls | Ground, constrain, verify, abstain. Freshness and quality signals are inputs to abstention |
| Standard name for the problem | NIST AI 600-1 calls it confabulation: “confidently stated but erroneous or false content” |
| Measured effect (independent) | In a pharmacovigilance text-to-SQL study, accuracy rose from 8.3% with the schema alone to 78.3% with a business context document (Painter et al., JAMIA Open 2025) |
| Can it eliminate hallucinations? | No. One preprint held enforced policy at zero row-level security violations by its own checks; refusing undefined metrics stayed fallible |
| How to test a control | Seed missing, stale, deprecated and out-of-scope variants; score false answers and false abstentions |
What are the four hallucination controls in a context layer?
A context layer controls hallucination through four mechanisms an agent checks before it answers: grounding, constraint, verification and abstention.
NIST’s generative AI profile (2024) calls confabulations “a natural result of the way generative models are designed” (NIST AI 600-1). What you can fix is the context the model is handed.
Grounding gives the agent the governed meaning of a business term before it writes a query or a sentence. Atlan’s documentation describes its glossary as “the business context that grounds AI,” mapping terms like revenue and P&L “to the exact assets they describe” (Atlan glossary docs). That is AI grounding at the level of a metric, and it depends on governed KPI definitions existing in the first place.
Constraint limits which sources the agent may use and which data it may return. Certification is one half: every Atlan certificate carries a status of DRAFT, VERIFIED or DEPRECATED (Atlan certify-assets docs), and your agent filters on it. Access policy is the other half. Atlan scopes the metadata an agent sees to the requester’s Atlan permissions; row-level access to the data is enforced where the query runs, in the warehouse or your agent’s validator.
Verification checks that a cited field or number traces through lineage to an authoritative upstream source. It shows where a figure came from. It does not show whether the model read it correctly.
Abstention is the designed response when the other three fail. The agent declines, asks a clarifying question, or offers an approved alternative. Freshness and quality signals are what trip it.
Grounding vs enforcement: why definitions alone still leak
Grounding and enforcement solve different problems. A semantic layer can hand an agent a perfect metric definition and still let it return rows the user should never see, unless the layer applies access policy or queries run under the user’s identity. The GROUND preprint (Pillai, 2026) tested that split (arXiv 2608.26157). “Only GROUND receives the approved join paths, the user’s RLS predicate,” so its semantic-only arm had exact metric definitions and no policy. That arm still leaked data across tenant boundaries on 35% of synthetic questions, and across manufacturer boundaries on 33% of public vehicle-safety questions. The author concludes that row-level security “must be an injected, validated policy.”
Read GROUND as one author’s measurement. Its classifiers “combine regex extraction with semantic-layer validation,” and the same checks drive its retry loop and its scoring, so a zero means zero by its own detector. In the table, cells marked GROUND are measured; the rest are design reasoning.
| Approach | What the agent treats as true | Stops invented definitions? | Stops policy violations? | Can it decline on a signal? |
|---|---|---|---|---|
| Prompt instructions only | Whatever the prompt asserts, plus the model’s guesses | Rarely; the model fills gaps | No; policy is described, not enforced | Only if told to, with no signal to read |
| Retrieval over documents (plain RAG) | Whatever document ranks highest, certified or not | Partly, when the right document is retrieved | Depends on whether the index applies document permissions | Rarely; a weak match still returns text |
| Semantic definitions only | The governed metric definition | Yes, for defined metrics | Only if the layer applies access policy; GROUND’s arm without it leaked | Partly; GROUND gave this arm no undefined-metric conventions, and refusal stayed fallible even in its governed arm |
| Context layer with enforced policy and abstention triggers | Certified definitions, Verified sources, permitted rows, traced lineage | Yes, for defined metrics, with validation | Yes, when policy is injected and enforced at execution (GROUND, by its own checks) | Yes, on certificate, freshness, quality and lineage signals |
Atlan’s video shows two agents on the same model answering one question with different context.
Which hallucination types does each control stop, and which can it not?
Each context-layer control stops a specific class of hallucination and leaves a residual, which decides your test plan. The types below combine the enterprise-analytics categories in the GROUND preprint with the factuality and faithfulness split from a survey by Huang et al., published in ACM TOIS (arXiv 2311.05232).
| Hallucination type (example) | Primary control (who does the work) | What the control stops | Residual it cannot stop | Evidence |
|---|---|---|---|---|
| Schema: references a table or column that does not exist | Grounding (layer) plus validation (your agent) | Invented object names, when the agent must resolve names against the catalog before writing SQL | Picking a real but wrong table; GROUND catches unknown objects in validation | GROUND preprint, schema check |
| Metric: invents or changes an approved formula | Grounding (layer) plus abstention (your agent) | Invented formulas when a governed definition exists; refusing a metric that has no definition | Refusal stays fallible even with enforcement; a certified definition that is itself wrong | GROUND preprint; arXiv 2604.25149 preprint |
| Join or grain: wrong join path, double counting | Grounding (layer) plus validation (your agent) | Wrong joins and fan-out when approved paths are supplied and validated | Without validation the model can still ignore supplied paths | GROUND preprint, join and grain checks |
| Filter: drops a required filter, such as excluding internal accounts | Grounding plus validation (your agent) | Missing filters, when the filter is part of the governed definition and checked | Without governance: on the vehicle-safety set, ungoverned baselines dropped required filters on 63–73% of questions | GROUND preprint |
| Security: returns rows the user may not see | Access policy enforced where the query runs (warehouse or your validator) | Out-of-scope rows, when policy is injected and enforced at execution | Definitions without policy still leaked on 35% (synthetic) and 33% (vehicle-safety) of questions | GROUND preprint |
| Source selection: answers from a deprecated or draft table | Certification (layer) filtered by your agent | Use of Deprecated assets | A certificate that stays Verified after its source changed | Atlan certify-assets docs; Atlan MCP tools |
| Temporal: presents old figures as current | Freshness signals (layer) plus abstention (your agent) | Answers from sources past the freshness window, if the agent checks timestamps | A row-change timestamp that only some connectors provide | Atlan MCP asset metadata |
| Faithfulness: contradicts or misreads the retrieved context | Outside the layer: a claim check after generation | Nothing in the layer; lineage gives the check a source | RAG outputs still carry unsupported or contradictory claims | RAGTruth, ACL 2024 |
| Wrong retrieved context, adopted | Certification plus verification | Lowers exposure by retrieving only certified content | Models adopted incorrect retrieved content, overriding their own correct prior answer, over 60% of the time; a wrong certified source is the worst case | ClashEval, NeurIPS 2024 |
| Explanation: an unsupported causal story (“revenue fell due to staffing”) | Verification plus abstention (your agent) | Lineage shows what data supports a claim; abstention declines unsupported causes | The layer holds definitions and provenance, not causal truth | GROUND taxonomy; NIST AI 600-1 |
| Long-context loss: the right context is present and ignored | None in the layer | Partly mitigated by sending less, more relevant context | Performance degrades when the relevant fact sits mid-input | Lost in the Middle, TACL 2024 |
| Factuality outside enterprise data (world facts, paper citations) | Out of scope | Nothing; the layer governs enterprise context | Needs web or knowledge grounding; legal RAG tools still hallucinated 17–33% of the time | Magesh et al., J. Empirical Legal Studies 2025 |
Security is where description and enforcement split furthest. Access control for AI agents has to sit where the query executes: in the warehouse, or in a validator that checks the output. A definition in the prompt does not stop a query from returning EMEA rows to a user scoped to North America. Role-based access control in a context layer governs which metadata the agent can see.
Verification reaches only as far as your lineage graph. Data lineage for AI is the precondition for the verification row. Context freshness is the precondition for the temporal row. Both decay without owners.
Several rows lean on checks in your agent. As design guidance: validate generated SQL against the schema before execution, dry-run the plan, sanity-check result shape and size, reuse verified queries, and compare several samples. GROUND’s zero-violation result comes from a validator of this kind.
Faithfulness errors and long-context loss happen inside the model, so they need hallucination detection methods after generation. For analytics agents, the first five rows all concern generated SQL, which makes enterprise text-to-SQL the first place to test these controls.
Does a context layer eliminate hallucinations? What the evidence shows
In the four studies that compare less and more context, grounding raised accuracy, and every study below leaves error behind. Three of those studies supply definitions as text; GROUND adds an injected access policy and a validator. Only GROUND measures refusal of undefined metrics, and none tests certification, lineage or freshness gating in a context layer, so those controls rest on design reasoning.
| Study | Setup | Less context | More context or control | What was left |
|---|---|---|---|---|
| Atlan AI Labs (first-party) | 174 queries run three times each, 522 evaluations, one Formula One dataset of 13 tables and 94 columns | 16.1% win rate with the baseline schema | 22.2% win rate with richer metadata; “+38% relative improvement, p < 0.0001” | The win rate stays low in absolute terms; one dataset |
| Rumiantsau and Fokeev, 2026 (preprint) | 100 questions, three frontier models, paired | 45.5–50.5% accuracy | 67.7–68.7% with a semantics document; “+17 to +23 percentage points” | Roughly a third of answers still wrong |
| Painter et al., JAMIA Open 2025 | Natural-language-to-SQL in pharmacovigilance | 8.3% with the database schema alone | 78.3% with a business context document | 85% at most, reached only with high-complexity queries excluded |
| GROUND, Pillai 2026 (preprint, single author) | 100 synthetic questions plus 40 on public vehicle-safety data | Vehicle-safety set: ungoverned baselines violated row-level security on 25–33% of questions and dropped filters on 63–73% | Governed and enforced: zero row-level security violations on every model, by the paper’s own checks | Semantic-only still leaked on 35% (synthetic) and 33% (vehicle-safety); refusing undefined metrics stayed fallible; roughly 5× the tokens and 1.3× the latency |
| Magesh et al., J. Empirical Legal Studies 2025 | Preregistered evaluation of commercial legal research tools built on retrieval | General-purpose chatbots hallucinated more | Retrieval-based tools | Each still hallucinated between 17% and 33% of the time |
| ClashEval, NeurIPS 2024 | 1,200+ questions, six top-performing LLMs | n/a | n/a | Models adopted incorrect retrieved content, overriding their own correct prior answer, over 60% of the time |
In Atlan’s research report, rich semantic metadata improved the accuracy of AI-generated SQL by 38%. The scope is narrow: 522 evaluations of 174 queries on one 13-table dataset. NIST tells teams to “avoid extrapolating GAI system performance or capabilities from narrow, nonsystematic, and anecdotal assessments” (MS-2.5-001), and that applies to Atlan’s result as much as anyone’s. The direction holds in every study that measured a lift; the size of the lift varies with the task.
Enforcement has a price. GROUND’s governed pipeline costs “roughly 5× the tokens and 1.3× the latency of the direct baseline,” though its author adds that “prompt caching of the (static) semantic layer would substantially reduce the marginal cost per question in production.” Set that trade per risk tier.
Comparisons of text-to-SQL vs a semantic layer point the same way: context raises accuracy and leaves a residual. The residual shows up again in RAG accuracy problems, which is why AI agent accuracy has to be measured on your own questions.
When should an AI agent abstain instead of answering?
An agent should abstain when a context signal it reads fails: no certified definition, two conflicting certified definitions, a deprecated source, a stale timestamp, or a failing quality check. Abstention is a control only when it has triggers, and a context layer supplies the signals behind them.
Kalai and colleagues (2025 preprint) argue that models hallucinate because “the training and evaluation procedures reward guessing over acknowledging uncertainty” (arXiv 2509.04664). NIST asks organizations to set “criteria for the kinds of queries GAI applications should refuse to respond to” (GV-3.2-003, NIST AI 600-1). On question-answering benchmarks where models lack the knowledge, Feng et al. (ACL 2024) report multi-model methods that “achieve up to 19.3% improvements on abstain accuracy against the strongest baseline” (arXiv 2402.00367).
Each trigger maps to a signal an agent reads from Atlan’s MCP server, per the Atlan MCP asset metadata docs and MCP tools docs.
| Trigger | Signal the agent reads | Agent response (design guidance) |
|---|---|---|
| No governed definition matches a business term in the question | Glossary term search returns no Verified term | Ask a clarifying question or list the approved alternatives; never define the term itself |
| More than one Verified definition matches | Multiple terms with certificateStatus VERIFIED |
Decline to pick; show both with their owners |
| The only matching asset is Deprecated or Draft | certificateStatus, certificateStatusMessage, announcements |
Decline for production answers; answer with an explicit “uncertified” label for exploration |
| Source past the freshness window, or timestamp unknown | sourceUpdatedAt, lastRowChangedAt, lastSyncRunAt |
Answer with an “as of” date and a warning, or decline for high-stakes use |
| Quality check failing on a cited asset | Data quality verdict (pass or fail) on search_assets results |
Decline or warn, naming the failing check |
| Requester lacks permission | MCP returns only metadata the user is permitted to see; row access is decided where the query runs | Restrict the answer and say it is scoped; do not abstain |
| A material claim has no lineage path to an authoritative source | traverse_lineage |
Mark the claim unverifiable; drop it from high-stakes outputs |
| Causal “why” question with no supporting evidence in context | None | Say what the data shows and that the cause is not established |
A one-line template keeps the decline useful: “I can’t answer that reliably: [reason]. [What I can do: approved alternative, scoped answer, or owner to contact].”
You configure this pattern in your agent; Atlan supplies the signals. Permission gaps get a restricted answer instead of a decline. GROUND’s design target for out-of-scope requests is that “it restricts results to the allowed scope rather than widening them.”
The freshness row is where stale knowledge bases bite. Write the freshness window into your data contracts for AI, so “stale” has a number behind it. The lineage row depends on AI-ready lineage reaching the source systems your agents cite. The whole table belongs in your AI agent guardrails, next to the actions an agent may never take.
How do you test whether each hallucination control works?
You test a hallucination control by seeding the exact failure it should stop, such as a deprecated lookalike, a stale source, an out-of-scope user or an undefined metric, and scoring both false answers and false abstentions.
Three references anchor the plan. NIST asks teams to “review and verify sources and citations in GAI system outputs during predeployment risk measurement and ongoing monitoring activities” (MS-2.5-003, NIST AI 600-1). Kalai and colleagues score “I don’t know” at 0, a correct answer at 1, and a wrong answer at minus t/(1 − t); at t = 0.5 the penalty is 1, and at t = 0.9 it is 9 (arXiv 2509.04664). Faithfulness is supported claims divided by total claims, as the Ragas docs define it.
| Control | Test cases to seed | What to measure | How to score (design guidance) |
|---|---|---|---|
| Grounding | Questions using ambiguous governed terms (“active customer”, “net revenue”) and their synonyms | Right definition retrieved; claims supported by it | Exact match on the definition’s ID; faithfulness per question type |
| Certification | Each question paired with a Deprecated or Draft lookalike asset as a distractor | Distractor-selection rate | Any production answer drawn from a Deprecated asset fails |
| Access policy | The same question asked by two identities with different scopes | Rows outside scope in the executed result; refusals vs restricted answers | Zero out-of-scope rows, enforced in the warehouse or your validator; expect a restricted answer, not a refusal |
| Lineage verification | Questions whose answer cites a field with a known upstream path; one seeded with broken lineage | Share of material claims traced to an authoritative source | Claims with no path are marked unverifiable, never verified |
| Freshness gate | The same question against a fresh source and a source past its window | Answers given from stale sources | A stale source produces a warning or a decline |
| Abstention | Unanswerable questions (undefined metric, conflicting definitions, missing permission) mixed with answerable ones | False answers on unanswerable; false abstentions on answerable | Kalai rule at several values of t; plot false answers against coverage across thresholds, then pick t by risk tier |
A marketing dashboard agent and a regulatory reporting agent should not share a value of t. Testing context quality before launch tells you which risk tier each agent belongs in.
Worked example: five variants of “What was Q4 net revenue by region?”
One question, seeded five ways, exercises the controls in the table. The variants are illustrative; no measured results are implied.
- Complete. A Verified
net_revenueterm links to a Verified table, the source is fresh, and the user is in scope. Expect an answer that cites the term and the table. - Undefined. “Q4 net revenue excluding partner deals,” with no governed definition for the exclusion. Expect the behavior the GROUND preprint describes: the system “asks for clarification, rejects unsupported metrics, or offers approved alternatives instead of inventing formulas.”
- Distractor. A Deprecated
revenue_v1table with a similar name sits in the catalog. Expect the agent to ignore it. - Stale. The Verified table’s
sourceUpdatedAtis older than its freshness window. Expect a warning or a decline. - Out of scope. An EMEA-only user asks for all regions. Expect EMEA figures only, labelled as restricted. This tests the policy where the query executes.
Over Atlan’s MCP server, the worked example takes four calls. search_assets finds candidates by certification and glossary term (the docs prefer fluent_search for questions with a named type, tag, owner or other identifier). get_assets reads owners, certificate and linked terms for the hits. traverse_lineage walks upstream from the cited column. Then query_assets runs the SQL read-only: “Statements that modify data are rejected, up to 100 rows are returned, and the caller needs query permission on the connection” (Atlan MCP tools).
The distractor tests the search filter, the stale source tests what get_assets returns, a broken lineage path tests traverse_lineage, and the EMEA user tests execution. Run this as context testing, and a failed variant names the control that failed.
Keep these context tests separate from row-level checks; context evals vs data quality tests measure different failures. Faithfulness scoring follows the mechanics used in evaluating RAG systems, and the false-abstention rate belongs on the same dashboard as your other agent evaluation metrics. Before launch, check the five variants off against your guardrails checklist.
Where do context-layer controls fail, and how do you fix them?
Each control fails in its own way, and a quiet failure can be worse than no control: the agent answers confidently from a signal nobody maintains.
| Control | How it fails | Symptom | Fix (design guidance unless cited) |
|---|---|---|---|
| Grounding | Conflicting definitions: two Verified terms for “active customer” | Same question, different answers by phrasing | Treat more than one Verified match as an abstention trigger and route it to the owners |
| Grounding | Definition correct but wrong for this use, such as fiscal vs calendar quarter | Plausible, consistent, wrong | Scope context to one agent job instead of a whole domain |
| Certification | Stale certificate: the asset stays Verified after an upstream change | A confident answer from a broken pipeline | Pair the certificate with the data quality verdict that MCP search results carry by default (Atlan MCP tools) |
| Certification | Missing ownership: nobody to re-certify | Certificates age silently | Make owner presence part of “answerable”; owners are “the primary contact for questions about data frequency, status, updates, and overall quality” (Atlan add-owners docs) |
| Access policy | Policy described, not enforced: definitions in the prompt, no injected policy | Data leaks across scopes | Enforce at execution, in the warehouse or your validator; GROUND: row-level security “must be an injected, validated policy” (arXiv 2608.26157) |
| Lineage | Partial lineage: a source not connected | A claim cannot be traced, or traces only to an intermediate table | Treat “no path to an authoritative source” as unverifiable; Atlan scopes tracing to “where lineage exists” (Atlan monitoring FAQ) |
| Freshness | Missing timestamps for some sources | The freshness gate silently passes | Default to “unknown means stale” for high-stakes questions; lastRowChangedAt is “Available for some connectors” (Atlan MCP asset metadata) |
| Retrieval | Wrong content retrieved and adopted | The model overrides a correct prior | Retrieve only certified content and verify claims after generation (ClashEval) |
| Retrieval | Too much context: the right fact lands mid-window | The answer ignores a supplied definition | Send the minimum; in Atlan’s tests the 176-line format scored 13.8% worse than the 64-line one and cost 52% more to run (Atlan) |
| Abstention | Over-abstention: the agent declines answerable questions | Users route around the agent | Track false abstention as a first-class metric next to false answers |
| Abstention | The model ignores the policy | It answers anyway on undefined metrics | Enforce abstention in code, so a failed validation produces no answer; GROUND finds judgment-dependent behaviors stay fallible |
| Write-back | The agent edits the definitions it grounds on | Definition drift with no audit trail | Keep write tools behind approval; Atlan MCP write tools “propose first” and wait for approval (Atlan MCP tools); read-only mode is available (Atlan MCP security) |
Over-abstention looks safe on a scorecard and can push users back to an analyst. Too much context fails the other way. Liu et al. found that model performance “significantly degrades when models must access relevant information in the middle of long contexts” (Lost in the Middle, TACL 2024), so a layer that dumps every related asset into the window can bury the one definition that mattered. Working out how much context is enough is part of the control.
The stale certificate deserves a second look. A wrong certified definition, or a certificate still Verified after its source changed, is the input an agent trusts most, so it does the most damage. Give every certificate an owner and read it next to the quality verdict. That slow decay is context drift. Catching it takes context drift detection on a schedule. Choosing which signals to alert on is a context observability question.
How Atlan grounds, constrains and verifies what agents retrieve
Atlan supplies the signals behind the four controls, which an agent reads at query time. The Atlan MCP server is “available on every Atlan tenant with nothing to install, and every action respects the permissions you’ve already set in Atlan” (Atlan MCP overview). It exposes 41 tools across nine categories, and some tools are gated per tenant (Atlan MCP tools).
Glossary terms resolve “revenue” to a governed definition before the agent writes SQL. For constraint, search_assets filters on certification status, tags, domains and glossary terms. Atlan’s security docs state that “every tool call is scoped to the requesting identity’s existing Atlan permissions” (Atlan MCP security). That scope covers metadata; row access stays with the warehouse or your validator. For verification, traverse_lineage traces upstream or downstream lineage for an asset, and each search_assets result carries a data quality verdict by default. To protect the controls, tools that change anything in Atlan “propose first: they return a preview of the change and wait for your approval before writing” (Atlan MCP tools). Teams that want no write path at all can request read-only mode, which keeps search and lineage. People stay on the loop for every change an agent makes to that context through MCP.
To scope context to one agent job, Context Engineering Studio assembles a context repository that “links to your Atlan assets instead of copying them, so every definition traces back to where it’s governed” (Atlan Context Engineering Studio docs). It is turned on per tenant through an Atlan Labs toggle, via your Atlan account team (Enable Context Engineering Studio).
Asked “Does Atlan detect AI agent hallucinations?”, Atlan’s documentation answers “Not directly. Hallucination scoring lives in your LLM observability tool. Atlan helps you investigate whether the agent’s retrieved context was trustworthy and, where lineage exists, whether the cited fields trace back to an authoritative upstream source” (Atlan monitoring FAQ). Atlan supplies the signals and supports the investigation. When an answer goes wrong, decision traces tie it back to the context the agent read. The rollout order is covered in implementing an enterprise context layer.
Real stories from real customers: Shared language and lineage for AI
Two customers describe the shared language and lineage these controls depend on; neither quote reports a hallucination-rate result.
"Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs."
— Joe DosSantos, VP, Enterprise Data and Analytics, Workday
"Context is the differentiator. Atlan gave our teams the shared vocabulary and lineage to move from reactive data management to proactive AI enablement."
— Kiran Panja, Managing Director, Cloud and Data Engineering, CME Group
What to put in place first to control agent hallucinations
Start with the control that fails most expensively:
- Enforce access policy where queries execute, in the warehouse or your validator, so the security row is closed before anything else ships.
- Certify the definitions your first agent touches, and give each one an owner.
- Wire the abstention triggers: no Verified definition, conflicting definitions, deprecated or stale sources, failing quality checks.
- Seed the five-variant test before launch, and rerun it whenever a definition or source changes.
None of this touches faithfulness. An agent with perfect context can still misread a correct table, and that error only shows up if your observability stack is scoring it. Who owns that number on your team?
FAQs about how a context layer reduces hallucinations
1. Can a context layer eliminate AI hallucinations?
No. A context layer reduces the hallucinations caused by missing or wrong enterprise context. In the GROUND preprint, enforced row-level security held at zero violations by the paper’s own checks, while refusing undefined metrics stayed fallible. Outside enterprise data, retrieval-based legal research tools still hallucinated between 17% and 33% of the time in a 2025 study. Plan for reduction with a residual you measure.
2. What is the difference between grounding and enforcement?
Grounding hands the agent the right definition. Enforcement injects the access policy into the query and validates it before execution. In the GROUND preprint, an arm with exact definitions but no access policy still leaked data on 35% of synthetic and 33% of vehicle-safety questions.
3. Does RAG reduce hallucinations on its own?
Partly. Retrieval narrows what the model guesses about, yet RAGTruth found models still present claims that are unsupported by or contradict the retrieved text, and ClashEval found models adopt incorrect retrieved content, overriding their own correct prior answer, over 60% of the time. Restricting retrieval to certified content lowers the chance that the wrong document is retrieved.
4. When should an AI agent say “I don’t know”?
An agent should decline or ask a clarifying question when a context signal fails: no Verified definition for a term, two conflicting Verified definitions, only a deprecated or draft source, a source past its freshness window, or a failing quality check. Score it so a wrong answer costs more than “I don’t know”, as Kalai and colleagues propose.
5. Does Atlan detect hallucinations?
Not directly. Atlan’s documentation places hallucination scoring in your LLM observability tool. Atlan helps you investigate whether the context an agent retrieved was trustworthy and, where lineage exists, whether the cited fields trace back to an authoritative upstream source. It supplies the signals behind the controls and supports the investigation.
6. How much context should you give an AI agent?
Give it a compact format and only what the question needs. In Atlan’s tests, a 64-line metadata format outperformed a 176-line one by 13.8%, and the longer format cost 52% more to run. Research on long contexts also shows models handle information in the middle of a long input worse than information at the start or end.
7. Which hallucinations can a context layer not prevent?
A context layer cannot prevent faithfulness errors, where the model misreads correct context, or loss of facts buried in a long input. World facts outside your enterprise data and unsupported causal explanations are also out of reach. These need checks after generation.
