> tpl_air_005
Agentic-RAG Workflow & Tool Contract
Production-grade specification for autonomous ReAct cognitive loops, strict JSON Schema tool calling contracts, execution sandboxing, human-in-the-loop intercepts, and RAG Triad evaluation.
Comprehensive autonomous agent architecture and financial concierge worked example. Covers ReAct state machine loops, OpenAPI tool schemas, WASM sandboxing, token budgeting, prompt injection defense, and TruLens grounding.
Important Tech Document Template & Operational Notice
TinyCTO.tv Tech Document Template Notice: This template is a general educational and operational starting point. It is not legal, tax, accounting, investment, procurement, regulatory, security or certification advice. Requirements vary by jurisdiction, organization, contract and risk. Review and adapt it with qualified professionals before relying on it.
Problem Solved
Prevents non-deterministic runaway loops, unauthorized database mutations, and prompt injection exploits in LLM-powered autonomous agents by enforcing strict tool contracts and sandboxed execution.
When to Use
- •When building LLM agents with multi-step reasoning and tool-calling capabilities.
- •When connecting autonomous agents to production databases, payment APIs, or internal services.
- •When deploying enterprise agents subject to AI governance and security oversight.
When NOT to Use
- •For simple single-turn chatbot interfaces that do not invoke external tools or mutate state.
5 Template Sections & Structural Outline
Target autonomy tier, mission boundaries, and golden KPIs.
Thought-Action-Observation loop with cycle-detection guards.
JSON Schema definitions with strict type validation.
Financial thresholds and sensitive mutation gates.
RAG Triad faithfulness assertions and fallback suppression.
Completion Instructions
Independent Review Checklist
- Are tool definitions strictly constrained with additionalProperties: false?
- Does the ReAct orchestrator enforce a hard ceiling on maximum loop iterations?
- Is high-value state mutation protected by human-in-the-loop step-up verification?
- Are prompt injection inputs actively rejected at the perimeter by an input classifier?
ApexConcierge Autonomous Banking Assistant
Fictional Entity: ApexGlobal Customer Financial Mesh
Production deployment of an autonomous banking concierge resolving balance lookups, currency conversion, and dispute initiation with 94.6% accuracy.
- •Automated 2FA interception on transfers over $1,000 USD.
- •Deterministic 4-step maximum ReAct cognitive loop preventing infinite cycles.
- •Langfuse OpenTelemetry tracing captures token spend and tool latencies.
Frequently Asked Questions
What is the recommended sandbox technology for agent tool execution?
WebAssembly (WASM) runtimes such as Wasmtime or Extism are recommended for CPU-bound data transformations due to sub-millisecond startup times and memory safety. For containerized network operations, lightweight ephemeral microVMs (e.g. Firecracker) provide hardware-assisted isolation.
Download Tech Document Pack
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Authoritative Sources
- ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022)Princeton University / Google Research • OFFICIAL RECOMMENDATION
- NIST AI 100-1: Artificial Intelligence Risk Management Framework (AI RMF 1.0)NIST • OFFICIAL RECOMMENDATION
- OWASP Top 10 for Large Language Model ApplicationsOWASP Foundation • INDUSTRY PRACTICE