The AI Could Keep Fixing Things. We Had to Decide When to Stop.
A PDF investigation shows how sensible suggestions from an AI agent can add up to unplanned work—and why deciding what is enough takes human attention.
Purpose
A PDF investigation shows how sensible suggestions from an AI agent can add up to unplanned work—and why deciding what is enough takes human attention.
AI agents use interfaces designed for people who bring context and learn through experience. What happens when they arrive without either?
Chicago's Deep Tunnel offers lessons for investing in AI defense, finding bottlenecks, and adjusting when future demands exceed our plans.
Alex Cannella applies familiar problem-solving methods to active AI incidents, smoldering problems, and conditions that could fuel future failures.
A retrospective lab notebook tracing experiments, measurement mistakes, and imperfect results when an AI must pause an unsupported action while continuing useful research.
Use disagreement with a coding agent to uncover missing context, preserve decisions, and improve how you work together.
Separate feelings, evidence, constraints, and assumptions when working with AI, while keeping research findings and practical hypotheses distinct.
How unspoken questions and missing dissent can prevent ideas from meeting—and what organizations can do to protect discovery.
How unchecked assumptions, unspoken thoughts, and gaps in shared context can steer an AI conversation—and how to make room for correction.
A newsletter access-control test prompted questions about login authority, lasting changes, and what an approval warning does not explain.
A reasonable question from an AI agent can arrive before we know enough to answer it. Decide when to commit, when a provisional choice is enough, and when to keep learning.
Why the next big win is not just faster workflow, but better work selection
Navigating the hype while meeting the future head on.
How rapid replacement separates useful life from market value, and what that means for buying, building, and switching technology.
After roughly a thousand issues, tickets appear to help humans and AI coding agents preserve constraints, failed approaches, and prior decisions. Shared memory also needs human care to stay useful.
A review of duplicated deployment work cut our deployment time by about a third. Periodic technical-debt reviews can reveal costs that have become routine, with humans deciding what to change.
AI can move the work, but people and organizations remain accountable for the choices, safeguards, and outcomes of its use.
Responsible AI use is a learning loop: anticipate what could go wrong, observe what happens, and use what you learn to improve the next decision.
Backup copies are a starting point. Recovering AI work also means restoring the conversations, instructions, connections, and context that make a project usable.
Recommendations need a receiving owner, a reasoned decision, and a way for learning to shape what comes next—even when the answer is no.
Repeatable habits keep human judgment involved when working with GenAI: develop the substance, pause to review, then give it shape.
Understand how flat skill names create ambiguity, and reduce friction and risk through source tracking, reviewed revisions, and deliberate updates.
Identify the last responsible gate where an unverified AI claim must be checked before action becomes hard to reverse.
Human-shaped model behavior raises choices about usefulness, safety, and the friction people face when those patterns are suppressed.
How independent recovery paths, retention, and tested restores help a business recover from mistakes, outages, and data loss.
Faster implementation changes when we can validate a system against the business it serves, and how we should plan to stay involved.
Shared Conceptual Vocabulary for Working with AI
When AI systems behave in unexpected ways, senior leaders need to examine the objectives, tools, permissions, monitoring, and organizational capacity surrounding the model.
Relevant context helps define an AI task through useful constraints, which makes a fitting answer more likely.
A plain-language explanation of why AI-powered second brains depend on maps, indexes, and human validation to find the right information.
A practical article on stress-testing AI-powered second brains for retrieval, source authority, calibration, and safe operating boundaries.
GenAI can expose the kind of technical debt where business meaning and implementation paths have drifted apart.
A practical how-to article on using transcript-based collaboration friction as one measurement tool for improving agentic work environments.
A field note on using shared operating principles to make human-AI collaboration more trustworthy.
AI-accelerated delivery needs a broader definition of coverage before teams can release with confidence.
AI collaboration gets interesting when old patterns are carried into a new working context.
Before asking for the implementation, collaborate on the design.
How Semantic Debt Compounds at Machine Speed
A Practical Pattern for Trustworthy Distinct Counts, Subtotals, and Grand Totals
A lesson from an AI agent that did not actually turn off.
What it took to turn scattered notes, an AI agent, and a shifting workspace into a collaboration that actually got better over time.
A useful answer is not proof that a chatbot knows, remembers, or has checked the answer.
A practical guide to clearer AI prompts through specific instructions, useful structure, roles, and iterative refinement.
You can catch contradictions without knowing the whole story.
The Dirty Secret of Free Text Overloading