ResultSeal
Fail-closed observation verification engine for agentic tool use. Prevents empty, partial, stale, or source-mismatched tool outputs from being promoted into factual claims in LLM agent loops.
Overview
Autonomous agents fail silently at the observation boundary: tool calls return empty strings, truncated stdout, stale cached states, or HTTP error messages that downstream LLMs rationalize into successful completions. ResultSeal eliminates this failure mode by placing a strict fail-closed verification barrier between tool execution and context ingestion.
Implemented in pure Python with zero external dependencies, ResultSeal binds observation data to deterministic evidence envelopes. Every tool result is validated against strict typed schemas, output integrity constraints, and execution freshness before it can be cited as evidence. If an observation fails any invariant, ResultSeal rejects it immediately with structured machine diagnostics, blocking downstream hallucination before it propagates through the agent loop.
Architected for high-frequency agentic loops, ResultSeal operates with sub-millisecond verification overhead (<0.1ms per seal) and strictly zero network roundtrips. It works out of the box with LangChain, LlamaIndex, the Claude Agent SDK, or custom runtime loops, providing deterministic integrity for production AI agents and frontier model evaluation harnesses.