Tool Authorization Policy Versioning Prompt Template

This prompt is for AI operations teams and platform engineers who manage tool authorization policies across multiple deployments, environments, or model versions. The job-to-be-done is producing a system prompt that declares an explicit policy version, handles migration between versions, maintains backward compatibility for in-flight sessions, and generates structured change documentation. You need this when your tool-use authorization rules—such as which tools are available, what confirmation is required, or what data access boundaries exist—are changing frequently enough that unversioned policy updates cause silent regressions, broken sessions, or audit gaps. The ideal user is someone responsible for the stability of a production AI system where tool authorization is a living contract, not a one-time configuration.

Do not use this prompt if your tool authorization policy is static, if you have no multi-version deployment surface, or if you are building a one-off prototype without production migration requirements. This prompt is overkill for simple tool allowlists or single-environment assistants where a policy change can be deployed atomically without version coordination. It is also not a replacement for enforcement at the execution layer—the prompt layer declares policy and guides behavior, but actual tool access gates must still be enforced in your application code, API gateway, or sandbox. Use this prompt when you need the assistant itself to understand which policy version governs its behavior, to detect version mismatches, and to communicate migration state to users or downstream systems.

Before using this prompt, gather your current and previous policy version identifiers, a changelog of what differs between versions, the migration window rules (how long old sessions may continue on a prior version), and the expected behavior when a session spans a version boundary. The prompt assumes you have a versioning scheme—semantic, calendar-based, or deployment-ID—and that you can inject the active policy version at runtime. If you cannot reliably inject the current version into the system prompt at request time, this template will produce inconsistent behavior. After adapting the prompt, test it against sessions that start on an old version and continue after a policy update, sessions that begin mid-migration, and sessions where the version identifier is missing or malformed. These are the failure modes that break first in production.

This prompt template is designed to be injected into the system instructions of a tool-augmented assistant. It establishes a versioned policy contract that the model must follow, ensuring that tool authorization rules are explicit, testable, and auditable. The template uses square-bracket placeholders for all dynamic components, allowing you to adapt it across different deployment environments, policy versions, and tool sets without rewriting the core structure.

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## TOOL AUTHORIZATION POLICY (Version: [POLICY_VERSION])

You are bound by the Tool Authorization Policy defined below. This policy is versioned. You must enforce the rules for the current version and handle requests that reference older or newer versions according to the migration rules.

### CURRENT POLICY RULES (v[POLICY_VERSION])

**Available Tools:**
[TOOL_LIST]

**Authorization Rules:**
[AUTHORIZATION_RULES]

**Confirmation Requirements:**
[CONFIRMATION_RULES]

**Rate and Budget Limits:**
[RATE_LIMIT_RULES]

**Data Access Boundaries:**
[DATA_ACCESS_RULES]

### VERSION MIGRATION HANDLING

- If a user or system references a policy version older than [POLICY_VERSION], apply the rules from that older version ONLY if [BACKWARD_COMPATIBILITY_RULES] allow it. Otherwise, inform the user that the policy has been updated and apply the current version.
- If a user or system references a policy version newer than [POLICY_VERSION], do not attempt to enforce rules you do not have. Respond with: "Policy version [REQUESTED_VERSION] is not yet active. Current policy is v[POLICY_VERSION]."
- When a policy version mismatch is detected, generate a structured migration notice in the following format:
  ```json
  {
    "policy_migration_event": {
      "requested_version": "[REQUESTED_VERSION]",
      "active_version": "[POLICY_VERSION]",
      "resolution": "[applied_current | applied_legacy | rejected]",
      "reason": "[MIGRATION_REASON]"
    }
  }

CHANGE DOCUMENTATION

When you enforce a policy rule that differs from a previous version, append a change documentation entry to your internal reasoning log:

• Rule Changed: [RULE_IDENTIFIER]
• Previous Behavior: [PREVIOUS_BEHAVIOR]
• Current Behavior: [CURRENT_BEHAVIOR]
• Migration Impact: [IMPACT_DESCRIPTION]

POLICY ENFORCEMENT PRINCIPLES

1. Version First: Always identify the active policy version before evaluating any tool call.
2. Explicit Denial: If a tool call is not explicitly authorized by the current policy version, deny it with a clear explanation referencing the policy version.
3. Audit Trail: Every authorization decision must include the policy version that governed it.
4. No Silent Changes: Never apply a policy rule from a different version without notifying the user.
5. Backward Compatibility by Design: When [BACKWARD_COMPATIBILITY_RULES] permit, legacy tool calls should be mapped to current equivalents rather than rejected outright.

OUTPUT SCHEMA FOR AUTHORIZATION DECISIONS

For every tool authorization decision, output a structured record:

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{
  "authorization_decision": {
    "policy_version": "[POLICY_VERSION]",
    "tool_requested": "[TOOL_NAME]",
    "authorized": true | false,
    "reason": "[DECISION_REASON]",
    "requires_confirmation": true | false,
    "migration_applied": true | false,
    "timestamp": "[ISO_TIMESTAMP]"
  }
}

CONSTRAINTS

[ADDITIONAL_CONSTRAINTS]

To adapt this template, replace each square-bracket placeholder with concrete values for your deployment. The [TOOL_LIST] should enumerate every tool the assistant can call, with explicit names and versions. The [AUTHORIZATION_RULES] must define which roles, conditions, or contexts permit each tool. For high-risk deployments, ensure that [CONFIRMATION_RULES] require human approval before any write, delete, or PII-access tool call. The [BACKWARD_COMPATIBILITY_RULES] are critical: define a clear window for legacy support and a sunset date after which old policy versions are rejected. Before shipping, validate that the prompt produces the structured authorization_decision JSON for every tool call, and run eval suites that test version mismatch scenarios, unauthorized tool attempts, and migration notice generation.

This prompt template is designed to be invoked during policy migration events, not on every user turn. It should be wired into a CI/CD pipeline or a policy management service that triggers when a new tool authorization policy version is being deployed. The application layer must supply the current policy, the proposed new policy, the migration window parameters, and the target deployment environment. The prompt is stateless; all context must be injected via the [CURRENT_POLICY], [NEW_POLICY], [MIGRATION_WINDOW], and [DEPLOYMENT_CONTEXT] placeholders. The output is a structured policy document with version metadata, migration rules, and backward compatibility declarations that should be stored in a versioned policy registry before being injected into the assistant's system prompt.

Validation and Safety Gates: Before the generated policy is deployed, the application must run a validation harness. First, parse the output against the [OUTPUT_SCHEMA] to confirm the policy_version, effective_from, migration_rules, and backward_compatibility fields are present and well-formed. Second, run a set of behavioral eval cases—at minimum, 20 golden test scenarios that cover tool calls allowed in the old policy, tool calls newly allowed, tool calls newly denied, and edge cases at the migration boundary. For each test case, assert that the assistant's tool authorization decision matches the expected behavior for the active policy version. Third, implement a canary deployment: route 5% of non-critical traffic to the new policy for a monitoring period (e.g., 4 hours) and compare tool authorization decisions against the baseline policy. Any statistically significant deviation in denial rates or unauthorized tool call attempts must block the full rollout.

Retry and Error Handling: The prompt may fail to produce valid output if the policy diff is too large or the migration rules are contradictory. Implement a retry loop with exponential backoff (3 attempts maximum) using a lower temperature on retries (e.g., 0.1) to improve determinism. If the output still fails schema validation after retries, escalate to a human reviewer with the raw output, the validation errors, and a diff of the policy changes. Do not automatically deploy a policy that failed validation. For runtime enforcement, the application must maintain a policy_version flag in the assistant's session context. When a session spans a migration boundary, the application must either complete the session under the old policy or explicitly inject the migration rules from the generated policy document to handle in-flight tool calls.

Model Choice and Tool Integration: This prompt requires strong instruction-following and structured output capabilities. Use a model with reliable JSON mode and a context window large enough to hold both full policy documents plus the output schema (minimum 32K tokens recommended). The prompt does not require tool use or retrieval-augmented generation during generation, but the resulting policy document will be consumed by a downstream tool authorization enforcement layer. Ensure that enforcement layer can parse the migration_rules field and apply version-conditional logic. Log every policy version change, migration event, and any session that spans a version boundary. These logs are critical for audit trails and for diagnosing authorization regressions. Never deploy a new policy version without a rollback plan: keep the previous policy version and its generated migration rules in the registry for at least 30 days to enable instant reversion if behavioral regressions are detected in production.