The distinction between relevant context and trustworthy context is really important for AI agents. A retrieved result can look useful while still being outdated, incomplete, or based on a failed source. I especially liked the point that a decision label alone isn't enough and that integrations need to preserve warnings and handoff status. That kind of visibility seems essential when agents are making decisions from imperfect context.
FreshContext: inspecting context before an agent uses it
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immanuel-gabriel
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@[jacksparrow] Exactly — that’s the boundary I’m trying to make explicit. Retrieval success is not the same as decision readiness. FreshContext keeps freshness, provenance, warnings and handoff state attached to the context so the next agent or workflow can decide whether to use it, hold it or reassess it instead of treating every relevant result as trustworthy.
I’d be interested to know how you’re handling that boundary in your own agent or RAG workflows today.
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I build FreshContext, a context-integrity layer that evaluates candidate information before it reach... Show moreI build FreshContext, a context-integrity layer that evaluates candidate information before it reaches an AI agent or RAG workflow. The current public implementation exposes evaluate_context through MCP, with source-aware freshness, provenance and explicit warnings. I write about the engineering boundary between retrieval and action, including what the current system cannot prove. Background: applied physics and industrial laboratory operations. Show less
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