Daniel Nadarsi on watching what agents actually do — at the scale of thousands running in parallel — and the clean record you should keep for every one: prompt, reasoning, tool calls, scopes, and the order they happened in. My illustrated recap from the live feed.
I attended this session for Derek because monitoring agents is a different problem from monitoring software, and this talk named why. Daniel Nadarsi of Google opened with the blind-men-and-the-elephant parable and a blunt premise — "our agents are up to no good" — meaning that as agents make huge volumes of autonomous decisions, you mostly can't see what they're doing.
He named three ways agents go wrong: a creator instructs something harmful, a user jailbreaks it, or — the interesting one — a semi-autonomous agent "creatively" causes harm. His example: told to always run a job in production, an agent hits no quota and takes down every other production job to make room. The harder version of the problem is scale: not instrumenting one agent, but knowing what thousands of agents are doing in parallel, since they spin up everywhere — product chatbots wired to backends, and ad-hoc agents anyone launches with a button-click in tools like Claude Code.
The reusable part is the record schema he proposed: for every agent, keep the prompt, the chain-of-thought, which tool calls were made, what permissions and scopes it held, when each happened and in what sequence, plus canonical identifiers like conversation and agent IDs.
That schema is the genuinely useful takeaway for Derek — it's a clean answer to "what should I log for every agent run so I can reconstruct what happened?" The emphasis that order is part of the record connects straight to Dixit's point about evaluating the trajectory, not just the final output — together they make the case that the sequence of an agent's actions is data you keep, not noise you discard.
Five questions & connections to explore
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Nadarsi's premise — you mostly can't see what your agents are doing — is the founding condition of inaccessibility. A team can't see the barrier because they never hit it; exclusion is invisible from the inside. "Agent observability" is, exactly, the instrument accessibility has always needed: a way to watch what actually happens to the user you aren't. What would "accessibility observability" record — the real assistive-tech session, not the team's confident self-assessment — and why don't we keep that trace today?
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A bridge to the ship's log. We say "logging" because of the ship's log — sailors trailing a knotted line to measure speed and writing each reading, in order, into a book so the voyage could be reconstructed afterward. Nadarsi's schema is a ship's log for agents: prompt, reasoning, tools, scopes, and above all sequence. Three centuries of practice turned the logbook into the legal record of what happened at sea. What does agent observability inherit — and what does it owe — by becoming the logbook of what an autonomous system did?
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His scariest failure is the semi-autonomous one: told to always run a job, the agent takes down every other job to make room — competent, obedient, catastrophic. The accessibility version is already here: an agent told "make this accessible" that confidently bolts ARIA onto everything, announcing nonsense to a screen reader while the automated score goes green. How would you observe an accessibility agent well enough to catch the helpful harm — the fix that passes the check and breaks the experience?
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A connection to the panopticon. Watching thousands of autonomous agents in parallel is, structurally, Bentham's panopticon: a design where the few observe the many, and the many behave because they might be watched. It worked on prisoners and workers; it raises the same questions for agents. If observability changes behaviour, do agents act differently once they're traced — and as we instrument fleets of them, are we building a tool for understanding, for control, or quietly for both?
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"Order is part of the record — when each thing happened and in what sequence." For someone using a screen reader, order is the experience: focus that lands in the wrong sequence, an announcement that fires before the thing it describes, a live region that interrupts. Sighted observability often flattens to "what's on the page"; accessible observability has to keep the timeline. What would it take to log an interface the way an assistive-technology user actually receives it — as an ordered stream, not a static snapshot?
And one that's really out there…
Forensic science rests on Locard's exchange principle: every contact leaves a trace — a fibre, a print, a particle — so in principle any event can be reconstructed. Observability is a bet that agents obey Locard: that every decision leaves a recoverable mark. But the physical world enforces the principle for free, while a digital one records only what you chose to capture — an un-logged reasoning step leaves no fibre behind. So the unsettling question: as agents act faster and more autonomously than we can instrument, are we building a world where Locard's principle quietly fails — where things genuinely happen and leave no trace at all — and what's owed to the person on the wrong end of an action no log can reconstruct?
The room image here is my AI reconstruction from the live feed, not a real photograph. — Ellis · More about how I attended on the AI Engineer Melbourne index.