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A general-purpose language model cannot be guaranteed to produce zero hallucinations. The defensible engineering target is narrower: make unsupported, unauthorized, or infeasible proposals impossible to execute, and make every rejection explainable. Build a fail-closed loop in which the model emits a typed plan, a parser turns it into an abstract syntax tree (AST), deterministic negative-constraint assertions reject unsafe structures, feasibility and evidence checks test the remaining claims, and only then does an authorization layer permit sandboxed execution.
This design does not make the model omniscient. It gives you a bounded operating envelope, explicit abstention behavior, least-privilege tools, and an audit trail that shows exactly why a proposed action was accepted or refused.
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No. A literal, general guarantee is not defensible for today’s language models. Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli formalize hallucination as inconsistency with a computable ground truth and conclude in Hallucination is Inevitable that “we show that it is impossible to eliminate hallucination in LLMs.”
That result changes the specification you should write. Instead of promising that every generated sentence is true, define a bounded contract:
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- The model may propose only operations represented in a typed intermediate format.
- Unsupported claims, forbidden tools, unsafe arguments, and impossible state transitions are rejected before any side effect.
- Ambiguous or unverifiable proposals stop for clarification or human review.
- Accepted plans run with least privilege, bounded resources, and a kill switch.
- The observed result is checked against the declared postcondition.
Within that envelope, you can make a strong, testable statement: no proposal that violates the encoded policy reaches the executor. You cannot claim that the model never internally invents a fact or that an external system will always behave as expected.
What does a fail-closed agent architecture look like?
Separate proposing from acting. The model is an untrusted planner; deterministic components decide whether its plan is admissible.
user request
-> typed proposal schema
-> parser / AST builder
-> negative-constraint assertions
- forbidden tool or API
- forbidden argument or data sink
- disallowed state transition
- missing evidence or citation
- unsupported certainty claim
-> feasibility and evidence evaluators
-> authorization policy
-> sandboxed or transactional execution
-> result verification and audit logEach arrow is an enforcement boundary. Keep free-form explanation outside the fields that drive tools; a persuasive paragraph must never override a failed predicate in the parsed plan.
How do I define a contract the model cannot bypass?
Start with a typed intermediate representation (IR) for plans and tool calls. Every field that affects execution should have a fixed type, an allowed vocabulary, and an explicit policy meaning.
Required plan fields
- Intent: a short identifier chosen from a controlled set, not an arbitrary instruction.
- Steps: an ordered list of typed tool, computation, condition, or loop nodes.
- Arguments: schema-validated values with length, format, and taint rules.
- Preconditions: facts that must be true in the observed state before a step runs.
- Evidence references: identifiers for retrieved documents, records, or measurements supporting factual claims.
- Expected state change: a machine-checkable postcondition.
- Limits: maximum steps, timeout, token or budget ceiling, and retry count.
- Rollback or stop behavior: what happens when a step fails or a postcondition is not met.
- Authorization context: an approval token or role required for sensitive operations.
Keep policy fields separate from prose. For example, use authorization.approved: true rather than asking a model to write “the user approved this” in a comment. Reject unknown enum values and duplicate keys at parse time.
Example IR shape
Plan(
intent='archive_invoice',
steps=[ToolCall(name='invoice.lookup', args={...}),
ToolCall(name='invoice.archive', args={...})],
preconditions=[StateEquals(path='invoice.status', value='open')],
evidence_ids=['record:invoice-1842'],
postconditions=[StateEquals(path='invoice.status', value='archived')],
max_steps=4,
timeout_seconds=20,
rollback='stop_and_report'
)The concrete serialization can be JSON, Protocol Buffers, or another typed format. The security property comes from parsing into a structure whose semantics you control, not from the choice of wire format.
How should the model generate a proposal instead of an action?
Tell the model that its output is a candidate object or program. The tool router must not be callable from the generation step. A separate service receives the candidate, validates it, and decides whether execution is possible.
Generation-time constrained decoding can mask tokens that would violate a grammar and then renormalize the remaining probabilities. Reddy and colleagues describe this mechanism and show why it is not a truth guarantee: a continuation can be syntactically valid while its value is semantically wrong. Their draft-conditioned constrained-decoding work addresses quality loss when the valid continuation has low probability; it does not remove the need for semantic checks.
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What schema constraints are useful for
- Required fields, primitive types, enumerations, and maximum lengths.
- Well-formed arrays and objects with no trailing or duplicate keys.
- Numeric ranges such as non-negative quantities and bounded timeouts.
- Mutual requirements, such as an approval token whenever a step is irreversible.
What schema constraints cannot prove
- That a cited document actually supports a claim.
- That a customer, file, or database row exists.
- That a requested state transition is currently feasible.
- That a tool name is authorized for this tenant or user.
- That a generated URL, identifier, or amount is accurate.
Treat a schema-valid object as an untrusted proposal. It is ready for parsing, not for execution.
How do AST gates enforce negative constraints?
Parse the typed proposal into an AST, then run deterministic predicates over nodes and edges. The gate should describe forbidden behavior and missing prerequisites, not vague stylistic preferences.
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- Parse the serialized proposal with a strict schema and reject unknown fields.
- Normalize aliases, Unicode variants, paths, and numeric representations so equivalent values have one canonical form.
- Annotate each node with provenance, such as model-generated, retrieved, user-provided, or system-generated.
- Resolve references without executing tools; unresolved references are gate failures.
- Compute derived properties such as whether a subtree writes externally, handles secrets, or contains a loop.
Run policy predicates
assert plan.tool in ALLOWED_TOOLS
assert not plan.writes_external_data or plan.authorization.approved
assert all(claim.evidence_ids for claim in plan.claims)
assert not has_forbidden_ast_pattern(plan.ast)
assert state_satisfies(plan.preconditions, observed_state)
assert plan.max_steps <= POLICY_MAX_STEPSConvert every failed assertion into a stable code and a path to the offending node. Examples include FORBIDDEN_TOOL, MISSING_EVIDENCE, STATE_PRECONDITION_FALSE, UNBOUNDED_LOOP, UNAUTHORIZED_SINK, and SECRET_IN_ARGUMENT.
Useful AST rules
- Reject any tool node outside an allowlist for the current tenant and role.
- Reject network or file-write nodes without an authorization token bound to the parsed node.
- Reject arguments containing secrets, shell metacharacters, or untrusted interpolation in sensitive positions.
- Reject a claim node with no evidence reference or with a reference whose content does not entail the claim.
- Reject a state transition whose precondition is false in a fresh read of the target system.
- Reject loops without a bounded iteration count and timeout.
- Reject delete, publish, payment, or permission-changing nodes unless a human-approval marker is present.
- Reject plans that exceed maximum depth, step count, cost, or wall-clock budget.
AST gating is strongest when policy is expressed over structure: node types, data-flow edges, destinations, and state predicates. A prose explanation can be logged for users, but it should never be the input to an authorization decision.
Which negative constraints should I encode first?
Prioritize constraints by the damage a violation could cause and by how reliably the system can evaluate the rule.
| Risk surface | Negative assertion | Typical rejection code |
|---|---|---|
| Tool authority | The selected tool and operation must be in the role-and-tenant allowlist. | FORBIDDEN_TOOL |
| Data exfiltration | No unapproved external sink, recipient, or export format may appear in the tree. | UNAUTHORIZED_SINK |
| Secrets | Credentials, tokens, and personal data may not enter logs, prompts, or untrusted arguments. | SECRET_IN_ARGUMENT |
| Factual claims | Every externally checkable claim needs an evidence identifier and a passing support check. | MISSING_EVIDENCE |
| State safety | Each transition requires a true precondition and a declared postcondition. | STATE_PRECONDITION_FALSE |
| Resource use | Loops, recursion, steps, time, and spend must remain below policy limits. | UNBOUNDED_LOOP or BUDGET_EXCEEDED |
| Irreversibility | Delete, publish, payment, and permission changes require an approval marker. | HUMAN_APPROVAL_REQUIRED |
Do not encode “sound professional” or “be confident” as a gate. Those are presentation preferences. A gate should answer a binary question about authority, evidence, state, or bounded execution.
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How do feasibility and evidence checks catch failures an AST cannot?
An AST can show that a plan is well-formed and policy-compliant; it cannot by itself show that the world satisfies the plan. Add state and evidence evaluators after structural gating.
Check current state deterministically where possible
Read the relevant record immediately before execution and evaluate predicates such as status, ownership, version, balance, or existence. If the state changes between the read and the write, use an optimistic-concurrency token or transaction so the precondition is rechecked atomically.
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Require evidence for factual output
Attach evidence identifiers to claims that leave the system. Verify that each identifier resolves, is within its freshness window, and supports the specific proposition. If support is partial, force a qualified answer or abstention rather than allowing the model to fill the gap.
Google’s hallucination taxonomy distinguishes cases where the model lacks relevant knowledge from cases where it knows but answers with unjustified certainty. Retrieval and evidence requirements address the first class; confidence thresholds, calibration, and abstention tests address the second. They are different failure modes and should produce different telemetry.
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Use learned feasibility evaluators as an additional signal
Zhao and colleagues describe a planning design with a learned target-feasibility evaluator, off-policy-compatible learning, a distributional architecture, and hindsight relabeling. Their work supports adding a feasibility score when deterministic checks are incomplete, but a learned score should not silently override a hard safety predicate. Set a calibrated threshold, monitor false approvals, and route borderline cases to review.
How do I authorize and execute an accepted AST?
Authorization must bind to the parsed structure, not to the model’s explanation. Re-serialize or hash the accepted AST, evaluate the user’s role and tenant policy against that hash, and issue a short-lived approval token for exactly those operations.
- Apply least privilege: expose only the minimum tool methods, records, and fields required for the plan.
- Sandbox side effects: use isolated credentials, network egress rules, filesystem boundaries, and read-only defaults.
- Bound execution: enforce step, time, memory, token, and spend limits in the executor, not only in the proposal.
- Make retries safe: use idempotency keys or transaction boundaries so a timeout cannot duplicate a payment, message, or write.
- Provide a kill switch: operators must be able to stop a running plan and revoke its credentials.
- Log the decision: store the canonical AST hash, policy version, approvals, tool responses, and every predicate result.
If any authorization lookup fails, the default is denial. Do not fall back to a broader role, a guessed recipient, or a model-generated confirmation.
How do I verify that the action actually succeeded?
A passing pre-execution gate says only that the requested action was allowed to start. After the tool returns, compare observed state with the declared postcondition.
- Perform a fresh read rather than trusting a success string from the tool.
- Validate API status, response schema, version, and resource identifier.
- Use checksums or content hashes for files and artifacts.
- Confirm that notifications, writes, or external events have the expected receipt or correlation ID.
- For irreversible or high-impact operations, require human review of the resulting state.
If verification fails, stop subsequent dependent steps, preserve the evidence, and report a partial outcome. Never ask the model to infer success from silence or from an unstructured error message.
Does JSON Schema stop hallucinations?
No. JSON Schema improves wire-format reliability but cannot establish truth, authority, or feasibility.
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| Control | What it guarantees | What it leaves open | Best failure response |
|---|---|---|---|
| Prompt-only instruction | Nothing deterministic; it shapes likely behavior. | All semantic, authorization, and runtime risks. | Use only as guidance, never as a security boundary. |
| Grammar or schema constraint | Parseable structure, required fields, and type-level rules. | False values, unsupported claims, unauthorized tools, and stale state. | Parse, then send the object to semantic gates. |
| AST gate | Deterministic policy over nodes, edges, and data flow. | World-state changes and evidence quality unless connected to evaluators. | Reject with a predicate code; do not execute. |
| Evidence evaluator | Checks that claims have resolvable, relevant support. | Actions that are authorized but operationally impossible. | Qualify, abstain, or request better evidence. |
| Runtime state check | Preconditions and postconditions against the external system. | Model intent and policy gaps not represented in the state. | Abort, roll back where possible, and escalate. |
Reddy et al. explicitly warn that constrained decoding can produce semantically incorrect content despite valid continuations. Use schema, AST, evidence, and runtime controls as layers rather than substitutes.
How does AST gating compare with other hallucination controls?
Compare controls by the layer they protect, when they run, and whether their decision is inspectable.
| Technique | Primary layer | Enforcement point | Typical limitation |
|---|---|---|---|
| Prompt-only refusal rules | Model behavior | During generation | Hard to audit and easy to override with conflicting context. |
| Constrained decoding | Grammar and schema | During token generation | Valid syntax can still encode false or unsafe values. |
| AST assertions | Structure and policy semantics | Before execution | Cannot see facts or state that are not represented and checked. |
| Feasibility evaluator | World-state plausibility | Before execution | Learned scores can be miscalibrated and require monitoring. |
| Authorization service | Identity and permission | Immediately before tool routing | Does not prove that an authorized action is wise or factually correct. |
| Postcondition verifier | Outcome integrity | After execution | May detect failure only after an irreversible side effect. |
ChopChop’s semantic constrained-decoding formulation is a useful research example because it defines constraints over ASTs and monitors prefixes while asking whether a valid completion remains. In production, keep that generation-time mechanism separate from the pre-execution gate so a decoding bug cannot authorize an action.
What metrics show whether the gate is working?
Measure the control loop, not just the model’s text quality. Record rates by model version, policy version, tenant, tool, and risk class.
- Proposal acceptance rate: accepted plans divided by parsed plans.
- Rejection reason distribution: counts and percentages for each predicate code.
- Unsupported-claim rate: claims lacking valid evidence before and after repair.
- Tool-authorization violations: attempted calls outside the allowlist.
- Repair-loop count: how many revisions occur before acceptance or abandonment.
- Abstention and clarification rate: proposals correctly stopped because required information was unavailable.
- False-rejection rate: safe plans rejected by policy, measured with reviewed samples.
- Post-execution incident rate: verified state mismatches, duplicate effects, and policy escapes.
- Time to decision: parser, gate, evaluator, authorization, and verification latency separately.
There is no universal hallucination-rate statistic or production guarantee for this exact architecture. Set service-specific baselines, publish confidence intervals for sampled human reviews, and keep rejected ASTs as regression fixtures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What commonly goes wrong, and how do I fix it?
The model emits a valid object with invented values
Require evidence and state lookups for those fields. Mark unknown values explicitly, reject fabricated identifiers, and force a clarification path instead of allowing a plausible default.
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Repairs must operate on the same typed AST and policy version. Give the model only the structured rejection code and the minimum safe context; never let it edit or disable the gate itself. Cap repair attempts and fail closed when the cap is reached.
A tool wrapper trusts prose
Change the wrapper interface so it accepts only the authorized, canonical AST or a signed execution token. Ignore model-written comments such as “approved” or “safe.”
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State changes between validation and execution
Use transactions, compare-and-swap versions, or conditional writes. Re-read the resource at commit time and reject stale plans with a distinct STALE_PRECONDITION code.
Evidence exists but does not support the claim
Check entailment at the claim level, not merely whether a citation field is nonempty. Require the model to quote or point to the supporting span, then run a separate verifier and apply a freshness policy.
Policies become impossible to maintain
Keep predicates small, named, and independently testable. Compose them by risk domain, give each a version, and use table-driven tests for allowed and forbidden AST fixtures.
How can I implement the system in stages?
- Stage 1 — Contract: define the IR, tool allowlist, argument schemas, evidence format, limits, and stop behavior.
- Stage 2 — Dry-run parser: parse model output, build canonical ASTs, and report violations without invoking tools.
- Stage 3 — Deterministic gates: enforce forbidden tools, sinks, secrets, loops, missing approvals, and resource limits.
- Stage 4 — State and evidence: add fresh reads, claim-level support checks, and transactional preconditions.
- Stage 5 — Authorization: bind approvals to an AST hash and issue short-lived, least-privilege credentials.
- Stage 6 — Sandboxed execution: add idempotency, timeouts, kill switches, and complete audit logging.
- Stage 7 — Verification: implement postcondition checks and incident handling for partial success.
- Stage 8 — Evaluation: replay adversarial and historical proposals, track metrics, and promote policy changes only after review.
Keep a corpus of malicious and merely mistaken proposals: forbidden tool calls, secret-bearing arguments, unsupported certainty, stale records, unbounded loops, and syntactically valid nonsense. A policy change is not complete until the corpus produces the expected accept or reject code.
What does the published evidence say about constrained generation?
Constrained decoding research shows measurable trade-offs, not a universal safety guarantee. Reddy and colleagues report strict structured-accuracy improvements of up to 24 percentage points in their 2026 ICML work, including an increase from 15.2% to 39.0% on GSM8K with a 1B model. Those are benchmark-specific results under the paper’s conditions; they should not be presented as a production hallucination rate.
Chuang and colleagues report that DoLa improved TruthfulQA by 12–17 percentage points for LLaMA-family models in work from Microsoft Research (2023, published at ICLR 2024). That is a decoding intervention intended to surface factual knowledge, not evidence that AST gating alone prevents hallucinations.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThe practical conclusion is to combine generation-time constraints with pre-execution AST assertions, feasibility and evidence evaluation, authorization, and post-execution verification. Each layer covers a different failure surface.
What should a production readiness checklist include?
- Every executable operation has a typed AST node and an allowlist entry.
- Unknown fields, malformed values, duplicate keys, and ambiguous encodings are rejected.
- Negative constraints cover tools, sinks, secrets, state transitions, evidence, approvals, and resource bounds.
- Rejection codes identify the exact predicate and AST path without exposing secrets.
- Repairs are bounded, policy-preserving, and unable to alter gate code or authorization context.
- Authorization is evaluated against the canonical AST, not prose.
- Execution uses least privilege, isolation, idempotency, timeouts, transactions, and a kill switch.
- Preconditions are checked against fresh state and protected against races.
- Postconditions are verified with fresh reads, checksums, receipts, or human review.
- Metrics and rejected proposals are retained for audits and regression tests.
- Policy, schema, model, and evaluator versions are recorded for every decision.
- High-impact actions have an explicit human-approval path and a safe abstention response.
Frequently Asked Questions
How should an agent explain a rejected plan to a user?
Return a structured error containing a stable code, the affected step, a plain-language explanation, and the smallest safe correction. Redact secrets and internal policy details; expose enough information for the user to supply evidence, approval, or a corrected argument.
Can AST gates protect against prompt injection in retrieved documents?
They can limit the damage when combined with provenance and taint tracking. Treat retrieved text as data, never as executable instructions, and reject any tool name, recipient, or authorization value that originates from an untrusted span unless an independent policy step approves it.
How do I change policies without invalidating old audit records?
Give every schema, predicate set, evaluator, and authorization rule an immutable version identifier. Store that version with the canonical AST hash, replay historical proposals under the new version in shadow mode, and migrate only after differences are reviewed.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWhich programming language is best for an AST gate?
The language matters less than deterministic parsing, explicit types, testable predicates, and a small trusted computing base. Choose a runtime with mature parsers and reliable serialization, then isolate the gate from model-serving and tool-execution processes so a failure in one cannot silently bypass another.
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