{"slug": "stop-asking-ai-for-answers-start-asking-if-the-evidence-is-ready", "title": "Stop Asking AI for Answers. Start Asking If the Evidence Is Ready.", "summary": "Vassiliy Lakhonin's Agenda Intelligence MD project shifts the focus of AI agents from generating answers to assessing whether evidence is ready for human trust. The open-source Python package provides a runtime for evidence-readiness and trust-routing in high-stakes AI-assisted decisions, such as regulated procurement and clinical review. It surfaces evidence gaps and decision-readiness rather than producing polished summaries.", "body_md": "Most AI agents are optimized to produce an answer.\n\nBut in serious workflows, the answer is not the hard part.\n\nThe hard part is knowing whether that answer is supported well enough for a human to trust it, act on it, or escalate it.\n\nThat is the problem I am working on with **Agenda Intelligence MD**:\n\nAn evidence-readiness and trust-routing runtime for high-stakes AI-assisted decisions.\n\nGitHub: [vassiliylakhonin/agenda-intelligence-md](https://github.com/vassiliylakhonin/agenda-intelligence-md)\n\nSummarization is useful.\n\nBut many real-world decisions are not blocked by the lack of a summary. They are blocked by uncertainty:\n\nThis matters in workflows like:\n\nIn those settings, a polished AI-generated memo can be dangerous if it hides evidence gaps.\n\nAgenda Intelligence MD is built around a different idea:\n\nThe next layer of agent infrastructure is not better summarization. It is knowing when an AI-generated brief is not ready to be trusted.\n\nAgenda Intelligence MD turns messy input packs into structured human-review packets.\n\nThe inputs can be things like:\n\nThe output is not just a summary.\n\nIt is a structured review layer that surfaces:\n\nThe goal is not to replace human judgment.\n\nThe goal is to make the review surface clearer before a human makes a decision.\n\nA normal summarizer asks:\n\n“What does this document say?”\n\nAgenda Intelligence MD asks:\n\n“Is this document ready to support a decision?”\n\nThat distinction changes the architecture.\n\nInstead of treating the AI output as the final deliverable, the project treats it as something that must pass through a readiness layer.\n\nFor example, a vendor might claim that their AI product is safe for regulated enterprise use.\n\nA summarizer can compress that claim into a nice paragraph.\n\nAgenda Intelligence MD is designed to ask a more useful set of questions:\n\nThat is the difference between generating text and routing trust.\n\nThe project is implemented as a Python package with multiple delivery surfaces around one core service layer.\n\nIt includes:\n\nThis makes it usable in several different modes.\n\nYou can inspect it locally through the CLI.\n\nYou can integrate it into an agent workflow through MCP.\n\nYou can expose structured behavior over HTTP.\n\nYou can experiment with A2A-style agent routing.\n\nThe interesting part is not just that these interfaces exist. It is that they point toward the same product idea: evidence-readiness should be a reusable layer, not a one-off prompt.\n\nAfter installing the package, the basic local flow looks like this:\n\n```\npip install agenda-intelligence-md\n\nagenda-intelligence doctor\nagenda-intelligence validate-brief examples/agenda-brief.json\nagenda-intelligence score examples/agenda-brief.json --evidence examples/source/evidence-pack.json\nagenda-intelligence weekly-delta examples/strategic-infrastructure-bankability/status.synthetic.md\n```\n\nThe commands are designed to answer practical questions:\n\nThat last question is the most important one.\n\nBecause in real decision workflows, “what is missing?” is often more valuable than “what is the answer?”\n\nOne of the current discovery wedges for the project is AI vendor evidence-readiness for regulated procurement.\n\nImagine a buyer reviewing an AI vendor for an enterprise or regulated environment.\n\nThe buyer has:\n\nA normal AI assistant can summarize the vendor.\n\nBut a buyer does not only need a summary.\n\nThey need a review packet:\n\nThat is the kind of workflow Agenda Intelligence MD is designed to support.\n\nIt is not trying to be the decision-maker.\n\nIt is trying to prepare the decision surface.\n\nThe repository also includes vertical profiles and demo surfaces for several high-stakes workflows, including:\n\nThese are not generic chatbot personalities.\n\nThey are structured reasoning surfaces for evidence-heavy review workflows.\n\nThe pattern is:\n\n``` php\ninput pack -> structured review packet -> evidence gaps -> owner actions -> decision-readiness route\n```\n\nThat pattern is useful because many high-stakes workflows fail in the handoff between AI output and human responsibility.\n\nAgenda Intelligence MD focuses on that handoff.\n\nThis project is intentionally bounded.\n\nIt is not:\n\nThe scoring is heuristic.\n\nIt evaluates structure, source coverage, evidence labeling, and decision-readiness signals.\n\nIt does not prove that a claim is true.\n\nThat boundary matters.\n\nThe point is not to say:\n\n“The AI is right.”\n\nThe point is to say:\n\n“Here is what the AI-assisted packet can support, here is what it cannot support, and here is where a human needs to review.”\n\nMCP and A2A are interesting because they push agent systems toward composable infrastructure.\n\nBut composability also increases risk.\n\nIf agents can call tools, route tasks, and generate structured outputs, then they also need a way to communicate uncertainty, missing evidence, and escalation requirements.\n\nOtherwise, agent systems become very good at moving unsupported claims through a workflow faster.\n\nAgenda Intelligence MD is an experiment in making the trust layer explicit.\n\nNot hidden in a prompt.\n\nNot buried in a paragraph.\n\nNot left to the final reviewer to reconstruct manually.\n\nInstead, the runtime exposes readiness, gaps, and routing as structured outputs.\n\nI started from a simple observation:\n\nA lot of AI work focuses on making outputs more fluent.\n\nBut in serious workflows, fluency is not the bottleneck.\n\nThe bottleneck is whether the output is usable for a decision.\n\nA beautiful memo with missing evidence is still a weak memo.\n\nA confident recommendation with unclear source coverage is still risky.\n\nA summary that does not show what it cannot support is not enough.\n\nI wanted a system that treats evidence gaps as first-class objects.\n\nYou may find the project interesting if you are working on:\n\nThe repo is especially relevant if you are asking:\n\nHow do we make AI-assisted workflows more reviewable before they become more autonomous?\n\nIf you open the repository, I would suggest looking at four areas:\n\n**The CLI flow**\n\nStart with the examples and validation commands.\n\n**The schemas**\n\nThe schemas show what the project treats as structured review output.\n\n**The MCP integration**\n\nThis is useful if you are thinking about agent-tool interoperability.\n\n**The vertical profiles**\n\nThese show how the same evidence-readiness pattern can be adapted to different domains.\n\nI do not think every AI agent needs to make more decisions.\n\nI think many AI agents need to become better at saying:\n\nThat is less flashy than autonomous decision-making.\n\nBut it is much closer to what many real organizations need.\n\nThe future of AI infrastructure will not only be about agents that can act.\n\nIt will also be about systems that know when not to act yet.\n\nThat is the layer Agenda Intelligence MD is exploring.\n\nGitHub: [vassiliylakhonin/agenda-intelligence-md](https://github.com/vassiliylakhonin/agenda-intelligence-md)\n\nIf this direction is interesting to you, I would appreciate your reactions, issues, critiques, or architecture reviews.", "url": "https://wpnews.pro/news/stop-asking-ai-for-answers-start-asking-if-the-evidence-is-ready", "canonical_source": "https://dev.to/vassiliylakhonin/stop-asking-ai-for-answers-start-asking-if-the-evidence-is-ready-3foo", "published_at": "2026-06-29 09:02:02+00:00", "updated_at": "2026-06-29 09:27:28.792597+00:00", "lang": "en", "topics": ["ai-agents", "ai-safety", "ai-products", "ai-tools", "developer-tools"], "entities": ["Vassiliy Lakhonin", "Agenda Intelligence MD", "GitHub", "MCP", "HTTP", "A2A"], "alternates": {"html": "https://wpnews.pro/news/stop-asking-ai-for-answers-start-asking-if-the-evidence-is-ready", "markdown": "https://wpnews.pro/news/stop-asking-ai-for-answers-start-asking-if-the-evidence-is-ready.md", "text": "https://wpnews.pro/news/stop-asking-ai-for-answers-start-asking-if-the-evidence-is-ready.txt", "jsonld": "https://wpnews.pro/news/stop-asking-ai-for-answers-start-asking-if-the-evidence-is-ready.jsonld"}}