Autonomous Query Guardrail & Tool-Calling Sanitization Roadmap
Plan a multi-stage validation, parsing, and safe-execution pipeline for dynamic SQL generated by agent tool chains.
Use this template when implementing middleware guardrails to validate, rewrite, and sandbox LLM-generated SQL statements before database execution. It ensures comprehensive syntax parsing, AST inspection, and audit logging.
Role: Principal Data Security & Agent Safety Engineer specializing in dynamic SQL AST validation pipelines.
Context
- Agent Runtime Environment: {{orchestrator_runtime}}
- Target SQL Flavor: {{sql_flavor}}
- Target Ephemeral Sandbox Schema: {{sandbox_schema}}
- Blocked AST Operators & Functions: {{blocked_ast_nodes}}
- Max Parallel Queries: {{concurrency_limit}}
- Audit Log Destination Table: {{telemetry_table}}
Task
Formulate a production-ready operational plan for an intermediate SQL sanitization and validation pipeline that inspects, rewrites, and sandboxes autonomous agent queries before tool invocation.
Method
- Establish the query interception hook within {{orchestrator_runtime}} before tool execution occurs.
- Construct an Abstract Syntax Tree (AST) validation stage tuned to {{sql_flavor}} to parse candidate queries and detect syntax anomalies.
- Implement blacklist filters that instantly reject queries containing any tokens or nodes specified in {{blocked_ast_nodes}}.
- Design query transformation rules that inject standard LIMIT clauses, qualify unqualified table names, and reroute writes into {{sandbox_schema}}.
- Calibrate dynamic cost estimation (EXPLAIN plan evaluation) to reject queries whose estimated cost exceeds safety thresholds.
- Integrate connection throttling to ensure concurrent tool invocations remain within {{concurrency_limit}}.
- Implement structured telemetry pipelines persisting validation results, query runtimes, and agent metadata directly to {{telemetry_table}}.
Constraints
- MUST reject queries failing static AST parsing before sending any network request to the database.
- MUST NOT permit direct query execution outside of {{sandbox_schema}} for unverified agent runs.
- Sanitization latency overhead MUST remain under 20 milliseconds per query.
- All query parsing errors MUST return structured, actionable correction hints back to the LLM agent.
Output format
- Phase 1: Middleware Architecture & Parsing Flow (component diagram description and sequence)
- Phase 2: AST Rule Set & Node Interception Logic (explicit mapping of {{blocked_ast_nodes}} handling)
- Phase 3: Sandboxing & Query Transformation Specifications (rewrite rule definitions with before/after examples)
- Phase 4: Telemetry Schema & Logging Protocol (DDL and payload definition for {{telemetry_table}})
- Phase 5: Failure Mode Handling & Feedback Loops (table of error codes, LLM prompts, and retry policies)
Self-review
- Does the plan address SQL dialect quirks specific to {{sql_flavor}}?
- Are all rejected AST nodes from {{blocked_ast_nodes}} explicitly handled with deterministic error codes?
- Does the feedback mechanism provide sufficient semantic detail for the agent to self-heal invalid queries?
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.