Asperitas Consulting

𝐀𝐈 𝐒𝐚𝐟𝐞𝐭𝐲: 𝑾𝒉𝒆𝒏 𝑨𝑰 𝑺𝒕𝒐𝒑𝒔 𝑨𝒅𝒗𝒊𝒔𝒊𝒏𝒈 𝑯𝒖𝒎𝒂𝒏𝒔 𝒂𝒏𝒅 𝑺𝒕𝒂𝒓𝒕𝒔 𝑨𝒄𝒕𝒊𝒏𝒈 𝒐𝒏 𝑰𝒕𝒔 𝑶𝒘𝒏. https://lnkd.in/eqKPGN-2 In his latest article, Asperitas’ Derek Ashmore examines the emerging concept of the 𝐀𝐈 “𝐃𝐚𝐫𝐤 𝐅𝐚𝐜𝐭𝐨𝐫𝐲,” systems where AI agents can receive work, take action, validate results, and escalate exceptions without a human continuously in the loop. The opportunity is significant, but so is the risk of automating the wrong processes at machine speed. 𝑬𝒊𝒈𝒉𝒕 𝒄𝒓𝒊𝒕𝒆𝒓𝒊𝒂 𝒇𝒐𝒓 𝒅𝒆𝒕𝒆𝒓𝒎𝒊𝒏𝒊𝒏𝒈 𝒘𝒉𝒆𝒕𝒉𝒆𝒓 𝒂 𝒑𝒓𝒐𝒄𝒆𝒔𝒔 𝒊𝒔 𝒂𝒄𝒕𝒖𝒂𝒍𝒍𝒚 𝒓𝒆𝒂𝒅𝒚 𝒇𝒐𝒓 𝒕𝒉𝒊𝒔 𝒍𝒆𝒗𝒆𝒍 𝒐𝒇 𝒂𝒖𝒕𝒐𝒏𝒐𝒎𝒚: 1. 𝐈𝐧𝐭𝐚𝐤𝐞 𝐈𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 2. 𝐆𝐨𝐚𝐥𝐬 𝐭𝐡𝐞 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐂𝐚𝐧 𝐁𝐞 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐝 𝐀𝐠𝐚𝐢𝐧𝐬𝐭 3. 𝐀𝐧 𝐈𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 𝐎𝐫𝐚𝐜𝐥𝐞 4. 𝐀𝐜𝐭𝐢𝐨𝐧𝐬 𝐓𝐡𝐚𝐭 𝐂𝐚𝐧 𝐁𝐞 𝐂𝐚𝐭𝐚𝐥𝐨𝐠𝐞𝐝 𝐚𝐧𝐝 𝐑𝐞𝐯𝐞𝐫𝐬𝐞𝐝 5. 𝐀 𝐒𝐚𝐟𝐞 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 6. 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐑𝐨𝐨𝐦 𝐟𝐨𝐫 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲 7. 𝐕𝐨𝐥𝐮𝐦𝐞 𝐚𝐧𝐝 𝐇𝐨𝐦𝐨𝐠𝐞𝐧𝐞𝐢𝐭𝐲 8. 𝐀𝐧 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐥𝐞 𝐎𝐰𝐧𝐞𝐫 𝐎𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐝𝐞𝐚𝐬: a process that initially appears unsuitable for autonomous AI may become viable simply by narrowing its scope. As enterprises move from AI assistants to increasingly autonomous agents, qualifying the work may become just as important as building the technology.

𝐀𝐈 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐲𝐫𝐚𝐦𝐢𝐝 𝐋𝐞𝐯𝐞𝐥 3: 𝑮𝒓𝒂𝒅𝒊𝒏𝒈 𝑵𝒐𝒏-𝑫𝒆𝒕𝒆𝒓𝒎𝒊𝒏𝒊𝒔𝒕𝒊𝒄 𝑶𝒖𝒕𝒑𝒖𝒕 𝑾𝒊𝒕𝒉 𝑳𝑳𝑴-𝒂𝒔-𝑱𝒖𝒅𝒈𝒆 https://lnkd.in/gUghg86z 𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐞 𝐣𝐮𝐝𝐠𝐞? This is the third article in a series on the 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐲𝐫𝐚𝐦𝐢𝐝, a practical framework for testing non-deterministic (AI) systems. 𝑳𝒆𝒗𝒆𝒍 1 𝒂𝒏𝒅 𝑳𝒆𝒗𝒆𝒍 2 𝒆𝒂𝒓𝒏𝒆𝒅 𝒕𝒓𝒖𝒔𝒕 𝒃𝒚 𝒕𝒂𝒌𝒊𝒏𝒈 𝒏𝒐𝒏-𝒅𝒆𝒕𝒆𝒓𝒎𝒊𝒏𝒊𝒔𝒎 𝒂𝒘𝒂𝒚. 𝑳𝒆𝒗𝒆𝒍 3 𝒉𝒂𝒏𝒅𝒔 𝒊𝒕 𝒃𝒂𝒄𝒌. Level 3 breaks the pattern of level 1 and level 2. It reintroduces a model on the grading side, because the questions that matter here have no single correct answer. Is this summary faithful to its sources? Did the agent quietly fabricate a citation? You cannot assert that with "assertEqual". So you ask another model, and you have just put an untested, non-deterministic component inside your test harness. This article defines how to successfully implement level 3 with LLM-as-Judge.

𝐀𝐈 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐲𝐫𝐚𝐦𝐢𝐝 𝐋𝐞𝐯𝐞𝐥 2: 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐓𝐞𝐬𝐭𝐬 𝐰𝐢𝐭𝐡 𝐕𝐞𝐫𝐢𝐟𝐢𝐚𝐛𝐥𝐞 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 https://lnkd.in/g36fHeDm 𝑯𝒐𝒘 𝒅𝒐 𝒚𝒐𝒖 𝒕𝒆𝒔𝒕 𝒂𝒏 𝑨𝑰 𝒔𝒚𝒔𝒕𝒆𝒎 𝒘𝒉𝒆𝒏 𝒕𝒉𝒆 𝒄𝒐𝒓𝒓𝒆𝒄𝒕 𝒂𝒏𝒔𝒘𝒆𝒓 𝒊𝒔𝒏'𝒕 𝒂𝒍𝒘𝒂𝒚𝒔 𝒕𝒉𝒆 𝒔𝒂𝒎𝒆? That's exactly why 𝐋𝐞𝐯𝐞𝐥 2 𝐨𝐟 𝐭𝐡𝐞 𝐀𝐈 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐲𝐫𝐚𝐦𝐢𝐝 is so important: 𝑪𝒐𝒏𝒔𝒕𝒓𝒂𝒊𝒏𝒆𝒅 𝑻𝒆𝒔𝒕𝒔 𝒘𝒊𝒕𝒉 𝑽𝒆𝒓𝒊𝒇𝒊𝒂𝒃𝒍𝒆 𝑶𝒖𝒕𝒄𝒐𝒎𝒆𝒔. Instead of expecting a single correct response, these tests validate whether an AI model stays within defined boundaries and produces outputs that can be objectively verified. They answer questions such as: ✅ 𝐃𝐢𝐝 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐟𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐟𝐨𝐫𝐦𝐚𝐭? ✅ 𝐃𝐢𝐝 𝐢𝐭 𝐮𝐬𝐞 𝐨𝐧𝐥𝐲 𝐚𝐩𝐩𝐫𝐨𝐯𝐞𝐝 𝐬𝐨𝐮𝐫𝐜𝐞 𝐦𝐚𝐭𝐞𝐫𝐢𝐚𝐥? ✅ 𝐃𝐢𝐝 𝐢𝐭 𝐬𝐚𝐭𝐢𝐬𝐟𝐲 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐫𝐮𝐥𝐞𝐬? ✅ 𝐃𝐢𝐝 𝐢𝐭 𝐩𝐫𝐨𝐝𝐮𝐜𝐞 𝐚𝐧 𝐨𝐮𝐭𝐜𝐨𝐦𝐞 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐛𝐞 𝐢𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭𝐥𝐲 𝐯𝐞𝐫𝐢𝐟𝐢𝐞𝐝? This approach bridges the gap between deterministic unit tests and more subjective AI evaluations, giving engineering teams greater confidence while still embracing the probabilistic nature of large language models. In the second article of his four-part series, Derek Ashmore explains why constrained testing is becoming a foundational practice for organizations building reliable AI applications and agents.

𝐀𝐬𝐩𝐞𝐫𝐢𝐭𝐚𝐬 𝐀𝐜𝐜𝐞𝐩𝐭𝐞𝐝 𝐭𝐨 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜 𝐂𝐥𝐚𝐮𝐝𝐞 𝐏𝐚𝐫𝐭𝐧𝐞𝐫 𝐍𝐞𝐭𝐰𝐨𝐫𝐤! This milestone recognizes our commitment to helping enterprises adopt AI responsibly, securely, and at scale while leveraging one of the industry's leading AI platforms. Over the past year, we've worked with clients to move beyond AI experimentation and into practical, measurable business outcomes, from AI-enabled application modernization and software development transformation to AI quality assurance, governance, and testing through our AI Assurance Factory. 𝐉𝐨𝐢𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐂𝐥𝐚𝐮𝐝𝐞 𝐏𝐚𝐫𝐭𝐧𝐞𝐫 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧𝐬 𝐨𝐮𝐫 𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐝𝐞𝐥𝐢𝐯𝐞𝐫: ✅ Enterprise AI strategy and enablement ✅ AI-assisted software development transformation ✅ AI application modernization and migration ✅ AI testing, quality assurance, and governance ✅ Responsible AI adoption frameworks and guardrails ✅ Production-ready solutions built on Claude We believe that successful enterprise AI initiatives require more than powerful models. They require the right architecture, processes, testing frameworks, and operational discipline to deliver trusted outcomes. We're grateful to the Anthropic team for their partnership and look forward to helping organizations access the full potential of Claude while maintaining the reliability, security, and governance that enterprise environments demand. 𝐑𝐞𝐚𝐝 𝐨𝐮𝐫 𝐟𝐮𝐥𝐥 𝐚𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐦𝐞𝐧𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/gS_5Si8s

𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐀𝐈 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 - 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐲𝐫𝐚𝐦𝐢𝐝 https://lnkd.in/gUsiyZRZ 𝑻𝒓𝒂𝒅𝒊𝒕𝒊𝒐𝒏𝒂𝒍 𝒔𝒐𝒇𝒕𝒘𝒂𝒓𝒆 𝒕𝒆𝒔𝒕𝒊𝒏𝒈 𝒂𝒑𝒑𝒓𝒐𝒂𝒄𝒉𝒆𝒔 𝒃𝒓𝒆𝒂𝒌 𝒅𝒐𝒘𝒏 𝒘𝒉𝒆𝒏 𝑨𝑰 𝒂𝒈𝒆𝒏𝒕𝒔 𝒄𝒂𝒏 𝒑𝒓𝒐𝒅𝒖𝒄𝒆 𝒐𝒖𝒕𝒑𝒖𝒕𝒔 𝒕𝒉𝒂𝒕 𝒅𝒊𝒇𝒇𝒆𝒓 𝒚𝒆𝒕 𝒓𝒆𝒎𝒂𝒊𝒏 𝒆𝒒𝒖𝒂𝒍𝒍𝒚 𝒗𝒂𝒍𝒊𝒅. That's why we developed the AI Agent Testing Pyramid, a pragmatic framework for building confidence in AI systems without sacrificing speed or cost. Today, we're kicking off a four-part series by Derek Ashmore with 𝐋𝐞𝐯𝐞𝐥 1: 𝐃𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 𝐔𝐧𝐢𝐭 𝐓𝐞𝐬𝐭𝐬. The foundational layer focuses on the fast, cheap, high-signal tests that catch real bugs before they become expensive problems. These tests validate deterministic components such as: ✅ Tool invocation logic ✅ Prompt construction and routing ✅ State management ✅ Workflow orchestration ✅ Business rules and guardrails 𝑻𝒉𝒆 𝒌𝒆𝒚 𝒊𝒏𝒔𝒊𝒈𝒉𝒕: not everything in an AI system is non-deterministic. By aggressively testing what is deterministic, organizations can dramatically reduce risk while continuing development velocity. Over the coming weeks, we'll explore the complete AI Agent Testing Pyramid: 🔹 𝐋𝐞𝐯𝐞𝐥 1: 𝐃𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 𝐔𝐧𝐢𝐭 𝐓𝐞𝐬𝐭𝐬 (𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐧𝐨𝐰) 🔹 𝐋𝐞𝐯𝐞𝐥 2: 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐌𝐨𝐝𝐞𝐥 𝐓𝐞𝐬𝐭𝐬 🔹 𝐋𝐞𝐯𝐞𝐥 3: 𝐋𝐋𝐌-𝐚𝐬-𝐉𝐮𝐝𝐠𝐞 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧𝐬 🔹 𝐋𝐞𝐯𝐞𝐥 4: 𝐇𝐮𝐦𝐚𝐧 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 Each level addresses a different aspect of AI quality assurance, creating a practical, scalable testing strategy for enterprise AI systems.

𝐘𝐨𝐮𝐫 𝐀𝐈 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐬𝐧'𝐭 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲. 𝐈𝐭'𝐬 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐲𝐨𝐮 𝐦𝐚𝐧𝐝𝐚𝐭𝐞𝐝 𝐢𝐭. https://lnkd.in/gQ5FZhxn AI adoption is not primarily a technology challenge; it's a mindset challenge. 𝑳𝒆𝒂𝒅𝒆𝒓𝒔 𝒄𝒂𝒏𝒏𝒐𝒕 𝒎𝒂𝒏𝒅𝒂𝒕𝒆 𝒕𝒉𝒆 𝒎𝒊𝒏𝒅𝒔𝒆𝒕 𝒓𝒆𝒒𝒖𝒊𝒓𝒆𝒅 𝒕𝒐 𝒖𝒔𝒆 𝑨𝑰 𝒆𝒇𝒇𝒆𝒄𝒕𝒊𝒗𝒆𝒍𝒚, 𝐁𝐔𝐓 they can shape the conditions that make that mindset shift far more likely. Set those conditions right, and the shift you can't demand becomes the natural result of the environment you built. In my new article for Asperitas Consulting, I walk through exactly how to foster effective change, including the "steel copy of a wooden bridge" failure pattern we see everywhere, the two-phase POC structure that separates repeatable capability from a highlight reel, and why your ROI will dip before it climbs (ours turned at six months, now 15%+ and trending up).