Latest EN articles from the Provenance Manifesto blog. https://provenancemanifesto.org/en/blog en Tue, 26 May 2026 00:00:00 GMT The dangerous version of the AI shift for QA is not that every tester gets replaced by a bot. The more realistic danger is quieter: QA gets reduced to checking the easy surfaces while the valuable quality questions move somewhere else. https://provenancemanifesto.org/en/blog/what-ai-output-testing-actually-looks-like https://provenancemanifesto.org/en/blog/what-ai-output-testing-actually-looks-like Tue, 26 May 2026 00:00:00 GMT AI agents require fundamentally different testing approaches than traditional software. Unlike deterministic systems, agents can produce multiple valid outcomes for the same input. Testing must shift from validating output alone to examining the entire reasoning process—including evidence discipline, memory reuse, governance boundaries, and decision traceability—to ensure decisions remain trustworthy even when reasoning is partially automated. https://provenancemanifesto.org/en/blog/testing-ai-agents-is-not-testing-software https://provenancemanifesto.org/en/blog/testing-ai-agents-is-not-testing-software Wed, 20 May 2026 00:00:00 GMT In the Realm of Delivery, the old guilds can suddenly forge software artifacts at impossible speed with the help of AI agents. Plans, code, summaries, tests, diagrams, and migration paths appear almost as soon as they are requested. The realm celebrates the new abundance until a dangerous pattern becomes visible: the artifacts remain, but the reasons behind them disappear. https://provenancemanifesto.org/en/blog/the-why-layer-book https://provenancemanifesto.org/en/blog/the-why-layer-book Wed, 20 May 2026 00:00:00 GMT Delivery Provenance Workspace can turn scattered delivery decisions into reusable management memory by capturing evidence, rationale, risks, and validation status in a Decision Provenance Assistant. https://provenancemanifesto.org/en/blog/delivery-provenance-workspace https://provenancemanifesto.org/en/blog/delivery-provenance-workspace Wed, 13 May 2026 00:00:00 GMT Software factories without preserved judgment are not senior engineering at scale. They are junior engineering at scale. Fast junior engineering. https://provenancemanifesto.org/en/blog/we-are-building-ai-software-factories-without-teaching-them-judgment https://provenancemanifesto.org/en/blog/we-are-building-ai-software-factories-without-teaching-them-judgment Wed, 13 May 2026 00:00:00 GMT Everyone talks about giving AI agents memory. Far fewer ask a harder question: What should an agent be allowed to remember? https://provenancemanifesto.org/en/blog/decision-provenance-threshold https://provenancemanifesto.org/en/blog/decision-provenance-threshold Tue, 21 Apr 2026 00:00:00 GMT Can AI agents recognize their own decisions? And can they reuse prior decisions as a kind of virtual gut feeling? https://provenancemanifesto.org/en/blog/decision-provenance-proof https://provenancemanifesto.org/en/blog/decision-provenance-proof Fri, 17 Apr 2026 00:00:00 GMT Why are we building large amounts of code if agents can orchestrate behavior with minimal logic? https://provenancemanifesto.org/en/blog/modern-agentic-architecture https://provenancemanifesto.org/en/blog/modern-agentic-architecture Sat, 11 Apr 2026 00:00:00 GMT We keep arguing about whether AI-generated text is “good” or “bad.” But that’s the wrong debate. What we’re actually seeing is a clash between different ways humans process information. https://provenancemanifesto.org/en/blog/ai-text-vs-human-perception https://provenancemanifesto.org/en/blog/ai-text-vs-human-perception Tue, 07 Apr 2026 00:00:00 GMT A practical guide to giving AI agents reusable decision memory through a logging threshold, a portable provenance contract, and file- or graph-backed storage so they can retrieve priors, preserve context, and update decisions as evidence changes. https://provenancemanifesto.org/en/blog/decision-provenance-how-to https://provenancemanifesto.org/en/blog/decision-provenance-how-to Sat, 28 Mar 2026 00:00:00 GMT What teams call intuition is usually not mystical talent. It is a human ability to reconstruct likely outcomes from previously seen decisions, assumptions, constraints, rejected alternatives, incidents, and trade-offs. In other words, gut feeling is a lossy local cache of decision provenance. Modern AI systems can retrieve documents, code, and tickets. But they rarely preserve the reasoning layer behind them. That is why RAG often retrieves relevant fragments yet still fails to explain why a system exists in its current form. https://provenancemanifesto.org/en/blog/gut-feeling-decision-provenance https://provenancemanifesto.org/en/blog/gut-feeling-decision-provenance Thu, 26 Mar 2026 00:00:00 GMT This article introduces a shared agent handoff protocol that standardizes how agents exchange context, ensuring stable inputs, outputs, and failure handling across multi-agent flows. It shows how the same contract enables sequential, parallel, and hierarchical execution modes without redefining communication patterns. The key idea is simple: define one consistent envelope and return schema, and you turn prompt chaining into a composable, observable system instead of fragile prompt passing. https://provenancemanifesto.org/en/blog/agent-context-and-exchange-protocol-how-to https://provenancemanifesto.org/en/blog/agent-context-and-exchange-protocol-how-to Thu, 26 Mar 2026 00:00:00 GMT Agents are not just smarter objects. OOP was designed for deterministic behavior; agentic systems operate through probabilistic reasoning, context, and runtime decision-making. That is why Agentic-Oriented Programming needs new primitives beyond classes and methods, especially around orchestration, memory, and decision provenance. https://provenancemanifesto.org/en/blog/agentic-oriented-programming-vs-oop https://provenancemanifesto.org/en/blog/agentic-oriented-programming-vs-oop Mon, 23 Mar 2026 00:00:00 GMT A practical guide to GitHub Copilot agent flows that explains instruction-layered agentic inheritance and compares sequential, parallel, and hierarchical orchestration patterns. https://provenancemanifesto.org/en/blog/agentic-flow-how-to-guide https://provenancemanifesto.org/en/blog/agentic-flow-how-to-guide Sun, 22 Mar 2026 00:00:00 GMT I asked AI to revise my English. It did a great job — cleaner, sharper, more readable. And then… it quietly inserted a Russian word into the middle of the sentence. Nothing broke. The sentence still made sense. You could read it and move on without noticing. That’s the problem. We’ve been trained to look for failures as crashes, errors, broken outputs. But AI doesn’t fail like that. It fails silently. It produces results that look correct — and that’s exactly what makes them dangerous. This is what I call the Fluency Paradox 👉 https://provenancemanifesto.org/en/blog/the-fluency-paradox-when-ai-sounds-right-but-stops-being-reliable https://provenancemanifesto.org/en/blog/the-fluency-paradox-when-ai-sounds-right-but-stops-being-reliable Sat, 21 Mar 2026 00:00:00 GMT This guide explains how to automate a Markdown blog into a multilingual publishing pipeline using GitHub Copilot Agents, where an orchestrator coordinates language subagents, updates README summaries, applies hooks and skills as guardrails, and produces reproducible, scalable outputs. https://provenancemanifesto.org/en/blog/building-an-automated-translation-pipeline-for-a-markdown-blog-with-github-copilot https://provenancemanifesto.org/en/blog/building-an-automated-translation-pipeline-for-a-markdown-blog-with-github-copilot Fri, 20 Mar 2026 00:00:00 GMT Here is another strong argument on something that has been quietly breaking beneath the surface of the AI wave - the relationship between knowledge and its origin. https://provenancemanifesto.org/en/blog/where-provenance-ends-knowledge-decays-reflections https://provenancemanifesto.org/en/blog/where-provenance-ends-knowledge-decays-reflections Tue, 17 Mar 2026 00:00:00 GMT Humans naturally resist sharing the reasoning behind their decisions because context and memory have historically been a source of influence and professional advantage. As a result, many critical decisions remain undocumented and live only in conversations or individual memory. In the AI-augmented era this becomes a serious governance problem, because systems evolve faster and the reasoning behind changes disappears even more quickly. Without preserved decision context, organizations lose the ability to explain, audit, or safely evolve their systems. The AI shift therefore turns decision provenance from a cultural preference into a structural requirement for organizational governance. https://provenancemanifesto.org/en/blog/provenance-is-not-about-tools-it-is-about-mindset https://provenancemanifesto.org/en/blog/provenance-is-not-about-tools-it-is-about-mindset Sun, 15 Mar 2026 00:00:00 GMT Once decisions become first-class artifacts, something fundamentally changes. When the environment evolves, we are no longer forced to rediscover the reasoning behind the system through archaeology and speculation. Instead, we can revisit the original decision, update the assumptions that are no longer valid, and regenerate the implementation in a way that reflects the new context. https://provenancemanifesto.org/en/blog/why_decisions_must_become_a_first_class_artifact https://provenancemanifesto.org/en/blog/why_decisions_must_become_a_first_class_artifact Sat, 14 Mar 2026 00:00:00 GMT Let's imagine a "Provenance-native company" in 2030 - an organization built from the beginning around decision lineage, SDLC memory, and AI execution traceability rather than trying to retrofit it later. https://provenancemanifesto.org/en/blog/2030_a_provenance_native_company https://provenancemanifesto.org/en/blog/2030_a_provenance_native_company Fri, 13 Mar 2026 00:00:00 GMT Following the release of the initial version of the Provenance Manifesto, I began examining whether existing market solutions align with principles outlined therein. https://provenancemanifesto.org/en/blog/why-organizational-memory-is-not-just-an-ai-knowledge-system https://provenancemanifesto.org/en/blog/why-organizational-memory-is-not-just-an-ai-knowledge-system Wed, 11 Mar 2026 00:00:00 GMT The article explores a simple but overlooked problem: software organizations rarely preserve the reasoning behind their decisions, even though those decisions shape everything they build. It argues that AI retrieval and documentation alone cannot solve this, because what’s missing is a structured system that records the relationships between decisions, assumptions, and outcomes. The Provenance Manifesto proposes treating decisions as first-class artifacts so organizations can preserve intent, accountability, and decision lineage as AI accelerates software development. https://provenancemanifesto.org/en/blog/the_day_the_provenance_manifesto_was_born https://provenancemanifesto.org/en/blog/the_day_the_provenance_manifesto_was_born Sun, 08 Mar 2026 00:00:00 GMT While building SDLC Memory, I ran into an unexpected architectural dilemma. Should the system reason like an autonomous agent, behave like a deterministic data transformer, or sit somewhere in between? I'm still deciding which direction is the right one for the MVP. https://provenancemanifesto.org/en/blog/git-for-decisions-needs-a-brain-but-what-kind https://provenancemanifesto.org/en/blog/git-for-decisions-needs-a-brain-but-what-kind Wed, 04 Mar 2026 00:00:00 GMT In "Part 1 - From RAG to Provenance: How We Realized Vector Alone Is Not Memory", we moved from RAG to Provenance, from similarity to lineage. But if AI agents will generate 50–80% of future work, the real question becomes: How does memory update safely? How do new decisions get validated, linked, and governed, instead of just embedded? This article shows the incremental graph update process behind the decision memory step by step, with a real example. Because in the AI era, memory must evolve, not just retrieve. https://provenancemanifesto.org/en/blog/from-rag-to-provenance-part-2-how-Incremental-graph-memory-actually-learns https://provenancemanifesto.org/en/blog/from-rag-to-provenance-part-2-how-Incremental-graph-memory-actually-learns Sat, 28 Feb 2026 00:00:00 GMT What if your SDLC doesn’t actually remember anything, and it only retrieves fragments? We’ve built powerful RAG systems that can surface “relevant” text in milliseconds. But relevance is not causality. And when something breaks in production, similarity won’t tell you why it happened, or which decision, risk, or dependency led there. In this article, I unpack why vector search alone is not memory, how graph structure changes the game, and how combining vector with a strict provenance model turns scattered documentation into something closer to organizational cognition. If you care about explainability, decision lineage, and real delivery intelligence - this one is for you. https://provenancemanifesto.org/en/blog/from-rag-to-provenance-how-we-realized-vector-alone-is-not-memory https://provenancemanifesto.org/en/blog/from-rag-to-provenance-how-we-realized-vector-alone-is-not-memory Sun, 22 Feb 2026 00:00:00 GMT Chapter Next: SDLC Memory & Provenance. In the previous chapters, we explored why SDLC has no real memory and why provenance must become structural, not optional. In this next step, we go deeper into a more uncomfortable question. What if the real bottleneck in delivery isn’t velocity, tooling, or even AI capability… but the biological limits of human context. Humans can actively hold about four meaningful constraints at once. Modern agents can process hundreds of thousands of tokens. And yet, neither can remember a living product over time without structure. This chapter connects cognitive science, AI context windows, and a practical Hot/Warm/Cold memory architecture to show why durable SDLC memory is not documentation overhead; it’s a competitive advantage. If execution is getting cheaper, memory is becoming the differentiator. Let’s talk about how to build it. https://provenancemanifesto.org/en/blog/why-humans-think-they-remember-everything-and-why-sdlc-memory-proves-they-dont https://provenancemanifesto.org/en/blog/why-humans-think-they-remember-everything-and-why-sdlc-memory-proves-they-dont Sun, 22 Feb 2026 00:00:00 GMT In the previous chapters, we spoke about SDLC Memory and Provenance as a way to reduce chaos, protect delivery integrity,and make decisions traceable inside engineering organizations. Now I want to zoom it out.Because if AI is changing how software is built, it is also changing something much bigger - how Intellectual Capital itself is valued. This article is not a deviation from the Provenance discussion. It is the next logical step. If execution becomes abundant, then memory, governance, and decision architecture become the real assets. Let’s talk about what happens to Intellectual Capital when AI materially replaces human positions, and what that means for companies that want to survive: https://provenancemanifesto.org/en/blog/how-should-intellectual-capital-be-assessed-in-the-context-of-artificial-intelligence-increasingly-replacing-human-roles https://provenancemanifesto.org/en/blog/how-should-intellectual-capital-be-assessed-in-the-context-of-artificial-intelligence-increasingly-replacing-human-roles Mon, 16 Feb 2026 00:00:00 GMT AI is rapidly taking over the “What” layer of software development — generating architectures, code, optimizations, and alternative solutions faster than humans ever could. As a result, implementation and solution exploration are becoming cheap, scalable, and increasingly automated. But the real strategic layer of engineering has never been the “What.” The critical questions are the “Why” — why a solution exists, why a trade-off was accepted, why a risk is tolerable, and why a particular outcome matters for the business. These questions define intent, not implementation. https://provenancemanifesto.org/en/blog/ai-will-take-the-what-but-humans-must-own-the-why https://provenancemanifesto.org/en/blog/ai-will-take-the-what-but-humans-must-own-the-why Tue, 10 Feb 2026 00:00:00 GMT As AI becomes capable of proposing architectures, writing code, and optimizing systems, the real danger is not malicious AI but losing track of the human intent behind the systems we build. Organizations already struggle to remember why decisions were made; in an AI-augmented environment this problem becomes much more serious because machines can optimize solutions faster than humans can understand them. To avoid building systems that perfectly optimize the wrong goals, we need a new infrastructure layer called Provenance—a structured record of decisions, constraints, trade-offs, and intent that links system behavior back to human purpose. Without such a memory layer, organizations risk becoming highly efficient but strategically misaligned, gradually losing the ability to explain or control the systems they create. https://provenancemanifesto.org/en/blog/we-are-teaching-ai-to-decide-but-we-are-forgetting-how-to-remember https://provenancemanifesto.org/en/blog/we-are-teaching-ai-to-decide-but-we-are-forgetting-how-to-remember Sat, 03 Jan 2026 00:00:00 GMT Software delivery organizations repeatedly lose the context behind their decisions. Months after implementation, teams often cannot explain why something was built, what trade-offs were made, or what was originally promised. This happens because SDLC tools track artifacts like tickets, commits, hours, and costs — but not the intent, commitments, and reasoning behind them. The result is “Context Amnesia : teams rebuild solutions, repeat decisions, argue about scope, and incur rework, margin loss, and burnout. The core problem is not careless teams but a systemic gap — SDLC has no built-in memory of decision rationale. The uncomfortable question the article raises is: why, in modern software development, do we rigorously track execution but not the reasoning that shaped it? https://provenancemanifesto.org/en/blog/why-sdlc-has-no-memory-and-why-delivery-teams-keep-paying-for-it https://provenancemanifesto.org/en/blog/why-sdlc-has-no-memory-and-why-delivery-teams-keep-paying-for-it Thu, 01 Jan 2026 00:00:00 GMT