LLMs and Accountability in Product Management

We have seen ~30% error reduction from even just using a distilled & fine-tuned model as judge In narrow applications, it’s almost effortless now to build a harness & sub 4b param model to execute task reliably. Most Consumer GPU’s can run a 4b param model at Q4 w/ decent context. You’d need some sort of coding ability - but you can literally ask ChatGPT or Claude “What do I need to know to enforce strict types & structured output from a small LLM ?” After that… it’s purely based on how good your prompt / harness / judge pipeline is. Harness should catch issues programmatically (way faster feedback to generation model), and the submission should go to judge once all tests pass harness. Arxiv is a great place to go learn about what makes models tick. #AI #OSS #Automation

Explicit contract checks in multi-tool high-stakes systems are a necessity of robust execution. At Next Step Fusion, we have explored this theme in the context of tokamak plasma control in "Robustness by Design: Interface Contracts for AI Control in High-Stakes Physical Systems" https://lnkd.in/eDYWAHWY

A new paper from Anduril on AI agent reliability validates what we've been building into NextMaps. Thanks Rhys P. for sharing it with me. RubricRefine (LeVine et al., 2026) argues that the dangerous AI failures aren't the ones that crash, they're the ones that complete and return a confident but wrong answer. Their fix: structured provenance checks and post-generation verification before you trust the output. Our WAMEX prospectivity pipeline has run this way from the start. Every claim in a report traces back to a source chunk from the historical A-file record. A dedicated verification pass runs after generation and checks the output against the source material before the report is delivered. If the data is thin, the report says so, it doesn't fill gaps with geology from training data. The paper is a good read if you're building AI systems where wrong outputs look identical to correct ones. That's the default in technical domains. Check it out for yourself, www.nextmaps.com.au

Standard AI agents learn through trial and error, but in defense, first-time errors are unacceptable. Lives may be at stake. In a new paper, Anduril engineers introduce a pre-execution reliability layer that verifies and repairs agent decision before any execution occurs. Initial testing shows rubric-based refinement allows small, open-source models with only one attempt to match the accuracy of frontier models with up to five attempts. This unlocks the user's ability to interpret the reliability of an agent's action plan through an evaluation of the rubric. The agent autonomously increases the reliability of the action plan by iteratively improving upon the feedback provided by the rubric evaluation. Without research, we can't solve the hardest problems in defense. Read the full paper on arXiv: https://lnkd.in/gmtbQDG5

Mathematical reasoning is the final frontier for autonomous business systems. Most LLM agents struggle with complex, multi-step logical deduction, often hallucinating when faced with high-level technical requirements. The new Research Math Agents (RMA) framework changes this by applying formal, rigorous reasoning to research-level math, moving beyond simple pattern matching. For operators, this represents a shift toward higher-reliability automation. When your agentic systems move from being 'stochastic parrots' to structured problem solvers, they can finally handle technical documentation, architectural reviews, and complex data analysis without human hand-holding. We are watching the integration of these reasoning frameworks closely. The future of operations isn't just faster task execution; it is autonomous decision-making backed by verifiable logic. Read the full paper here: https://lnkd.in/esSGw7aP

Medium Drop #3 on our layer 2 is live. 📣 “Deterministic Decomposition: Turning Intent into Executable” explores a core problem in AI systems: How do you reliably transform ambiguous human intent into structured, executable workflows? 🤖 The article breaks down why deterministic decomposition matters for: • agent reliability • orchestration accuracy • reproducible execution paths • scalable AI infrastructure A key takeaway: LLM capability alone isn’t enough — systems need deterministic planning layers that can consistently map intent into actions. This piece dives into the architecture patterns behind that transition from “understanding” to “execution.” Read the full article here: https://lnkd.in/gTdAaq4n

I just read a paper that reframes how we should build agent skills. Current approach: hand-write a prompt, generate it once, or let the agent loosely revise itself. None of those reliably improve. Most degrade. SkillOpt treats the skill document as external state to be trained. Not written. A separate optimizer model proposes bounded add, delete, and replace edits. A validation gate only accepts changes that improve a held-out score. Results on GPT-5.5: +23.5 points over no-skill baseline. +24.8 inside Codex. +19.1 inside Claude Code. It beat human-written skills, one-shot LLM skills, and five competing methods across all 52 evaluated cells. The optimized artifact is a compact markdown file. It transfers across model scales and execution harnesses. Zero inference-time cost. The shift feels familiar. We moved from hand-coding features to gradient descent. Now we are moving from prompt engineering to text-space optimization. How long until your team's agent skills are obsoleted by trained ones?

📚 RAG at 10 Million Documents Is No Longer a Prompting Problem — It’s a Systems Design Problem. If you are planning a major enterprise knowledge management with RAG for your organization, the article below makes a strong point: once you scale RAG to 10 million documents, the usual “embed, retrieve, generate” approach breaks down. Latency rises, retrieval quality drops, hallucinations compound, and even powerful models cannot recover from bad chunks. How then do you scale and keep the growing knowledge in check and balance? The real differentiator at scale is not the frontier model — it is retrieval quality, verification, and operational discipline. URL : https://lnkd.in/gr8cEE2W Key takeaways 1. Hybrid retrieval is essential Combine BM25 + embeddings, then rerank results. Exact clause numbers, keywords, and structured references often fail with embeddings alone. 2. Use a two-stage retrieval pipeline ANN search gives speed, while cross-encoder reranking gives precision. This is what makes large-scale retrieval practical and accurate. 3. Confidence scoring should gate generation If retrieval confidence is too low, the system should refuse to answer rather than guess. 4. Constrain the LLM hard The model must answer only from provided context, cite sources, and explicitly say when information is insufficient. 5. Citations and hallucination checks are non-negotiable Enterprise-grade RAG must be verifiable, auditable, and able to detect unsupported claims before users trust the answer. The important message: Success of a RAG for knowledge management is not achieved by a bigger model — it must be designed into the pipeline. Best thing is that it provides a demo site for you to run and test all the above concepts. Try it out today with your test documents such as published research and journal articles. #RAG #EnterpriseAI #AIArchitecture #RetrievalAugmentedGeneration #LLMOps #KnowledgeManagement #AIGovernance #ScalableAI

I’ve been working on HACP: Human-Approved Coordination Protocol. The core idea is simple: AI agents should not quietly borrow more trust than a human gave them. As teams start using coding agents, review agents, CLI tools, and workflow automation, the hard problem is not just “can the agent do the task?” It is: Who authorized this? What exactly was approved? What evidence came back? What changed? What still needs a human decision? What did the agent request versus what did a human actually approve? HACP is my attempt to make that boundary explicit. It does not execute work. It does not approve outcomes. It records authority, evidence, custody, and human decisions around delegated AI work. The first slice we are dogfooding is around AI-assisted engineering workflows: PR review loops, bounded CLI reports, evidence packets, and human merge decisions. The goal is not to replace skilled humans. It is to keep skilled humans in control while agents do more of the prep, evidence gathering, and follow-up work. More soon. http://www.hacp.io

One of the biggest assets inside any engineering team is knowledge. Past defects. Production incidents. Release learnings. Testing strategies. Root cause analyses. The challenge is that this knowledge is often scattered across tools and documents, making it difficult to use when it is needed most. As AI adoption grows in testing, a key question becomes: How can teams make better use of this knowledge? That is one of the reasons RAG has become an important topic in Quality Engineering discussions. Join us in 2 days as Janani Balasubramanian shares practical insights on this evolving space. 📅 4th June 2026 ⏰ 9 PM IST https://luma.com/qhhhn6in