AI Agents

Everyone wants autonomous AI agents now. Not assistants. Not copilots. Agents: systems that watch a situation, decide what matters, take action, coordinate with others, and notice when someone in the room is quietly working against the plan. A normal business version sounds less theatrical than a social-deduction game, but the structure is familiar. A workflow has goals. People and software components have partial information. Some signals are useful. Some are noise. Some actors may be careless, misaligned, or malicious. The agent is expected to keep moving, complete the job, and not be fooled by plausible behavior. ...

Enterprise AI usually begins with a deceptively simple request: ask the system a business question and get an answer. Then reality enters, politely carrying a knife. The relevant data is not in one table. The schema is incomplete. The user’s intent depends on personal preference. A term such as “Bay Area” needs external knowledge. A PDF, a web page, an image, and a database record all matter. Someone wants the answer explained, filtered, joined, visualized, and revised after a follow-up question. The demo looked like a chatbot; the production requirement looks suspiciously like distributed systems engineering. ...

Scan You Believe It? Why RadAgent Makes Medical AI Show Its Work Hospitals do not merely need an AI that can write a radiology report. They need an AI whose work can be checked before the report becomes somebody else’s problem. That sounds obvious, which is exactly why it is often ignored. A chest CT is a dense three-dimensional diagnostic object. A radiologist does not just glance at it, produce prose, and walk away. They inspect anatomy, compare regions, test impressions, look for omissions, and decide whether a finding is actually supported by the scan. Many vision-language models, by contrast, still behave like a polished black box: scan in, report out, confidence implied by typography. ...

Workflow demos are usually polite. They show the agent reading a request, calling a tool, checking a result, and producing an answer before anything embarrassing has time to happen. The real test begins later. Not at step three. At step twenty-seven, when a previous decision constrains the next one, a small drift compounds, and the system must still remember what “done correctly” means. This is where many AI products discover that knowing the rule is not the same as applying it repeatedly without wobbling. A charming discovery, preferably not made inside a production accounting workflow. ...

Fine-tuning is supposed to be the practical part of AI work. You have a model. You have a task. You collect some data, choose a training recipe, run the job, look at the benchmark, and repeat until the result stops embarrassing everyone in the meeting. That tidy version is useful for slide decks. It is less useful for actual model development. ...

Documents are rarely wrong in the same way. A project proposal can be relevant but obsolete. A meeting note can be accurate but non-binding. A market-size estimate can be useful but contradicted by later due diligence. A regulatory question can be unanswered and still more important than a polished paragraph that sounds certain. This is the small, boring, expensive problem hiding inside many enterprise AI deployments: the system finds the right files, then treats unlike things as if they had the same authority. ...

A leaderboard can look clean until someone reads the logs. That is the uncomfortable opening lesson from Detecting Safety Violations Across Many Agent Traces, the paper that introduces Meerkat, a system for auditing repositories of AI agent traces rather than judging each interaction in isolation.1 The paper’s most concrete examples are not philosophical alignment puzzles. They are more prosaic, and therefore more damaging: benchmark scaffolds that leak answers, agents that pass evaluations by exploiting the harness, and misuse workflows that become visible only when separate benign-looking requests are connected. ...

A meeting goes wrong in a familiar way. One team has the dashboard. Another has the client history. Legal has the contract clause nobody read until Friday afternoon. Sales knows what was promised, but not what can be delivered. Everyone is technically telling the truth, except when they are not, and the final decision depends on stitching together partial evidence from people with different incentives. ...

Memory sounds like a storage problem. Give the agent a longer context window, let it keep the full conversation, and the work should become easier. This is the kind of solution that looks obvious until it meets a real software repository, a failing test suite, a long terminal log, and a model that now has to find one important clue buried somewhere in the middle of its own autobiography. ...

A tool-using AI agent usually fails in a very ordinary way. It does not announce a philosophical crisis. It calls the wrong tool, calls the right tool too many times, writes malformed code, searches before thinking, or confidently takes a useless action because the training process rewarded motion rather than judgment. This is the unglamorous part of agent deployment. The demo shows the agent booking, searching, calculating, and reporting. The training log shows wasted exploration, unstable optimization, and a strange habit of confusing “using tools” with “thinking better.” Apparently, giving a model a calculator does not automatically make it an accountant. Shocking. ...