Methodology · Evaluationemergingverified

Real-World Agent Trial

also known as agent field trial, live capability probe

Applies to: agentmulti-agent-systemcoding-agentbrowser-agent

Tags: field-trialobservationcapability-probe

Try a candidate agent on real, open-ended tasks before you give it to production users. The tasks are realistic, such as office work, strategic games, and problems that mix text, images, and other inputs. The trial is about watching. People observe how the agent handles full tasks under real conditions. They log where it works and where it fails. From that, they write down what the agent can and cannot do, backed by what was actually seen, not by benchmark numbers. This adds to per-ability and whole-system tests. It does not replace them. It surfaces failures that made-up test sets miss.

Methodology process overview

flowchart TD agent[Candidate agent build] --> s2[Instrument for observation] domain[Use domain] --> s1[Curate realistic tasks] s1 --> tasks[Task catalogue - heterogeneous, open-ended] s2 --> instr[Trajectory capture - tools, reasoning, retries] tasks --> s3[Run trials with human observers] instr --> s3 observers[Domain-aware observers] --> s3 s3 --> obs[Observed runs with annotations] obs --> s4[Extract claims with run anchors] s4 --> caps[Capability claims] s4 --> cons[Constraint claims] s4 --> unanchor{Claim has logged run?} unanchor -->|no| drop[Reject claim] unanchor -->|yes| caps caps --> s5[Feed back to evals and gating] cons --> s5 s5 --> regress[New regression eval cases] s5 --> rollout[Scoped autonomy tier]

Intent. Find out what an agent really can and cannot do by watching it work through real, open-ended tasks under field conditions.

When to apply. Use this before you commit to a wider rollout of a non-trivial agent. It helps most when the made-up tests feel untrustworthy, or when people disagree about what the agent can actually do. Don't apply it for narrow agents whose behaviour an existing test set already covers fully. Skip it too for one-shot generation systems, where there is no run to watch.

Example scenario

A bank is sizing up a general-purpose office-assistant agent for internal back-office work before any rollout decision. Made-up tests show 92% on their curated task set. But the executive sponsor and the head of operations disagree on whether the agent is really ready. The sponsor saw a clean demo. Ops heard from a pilot user that the agent 'kind of melts down on real emails'. They ran a trial to settle it on evidence. They put together a varied task catalogue. Draft a reply to a regulator's information request. Schedule a six-person meeting across two time zones. Summarise an hour-long compliance training video. Play through a strategic-game scenario that tests multi-step reasoning. Triage a backlog of 30 misrouted customer emails. The agent was set up to record full runs: every tool call, every retry, every reasoning step in between. Four reviewers who knew the field sat with the agent for half a day each and took notes as they watched. They were a compliance officer, an executive assistant, a training-content owner, and a customer-ops lead. The trial produced 11 capability claims. One example: 'reliably drafts regulator replies under 400 words when given the source filing as input, see runs 04, 09, 11'. It also produced 7 constraint claims. One example: 'fails to spot implied deadlines in customer emails; 6 of 30 misrouted cases mis-prioritised, runs 21-26'. The constraints went straight into the test set as new regression cases. The rollout decision shifted from 'broad pilot' to 'narrow pilot on regulator-reply tasks only', with email triage held back until the missed-deadline failure could be fixed. The exec sponsor and the ops head agreed on the next step, because every claim had a run id behind it.

Inputs

Candidate agent — The agent build to be tried, set up so its actions can be observed.
Realistic task catalogue — Open-ended tasks from the intended use area: office workflows, strategic games, and problems that mix text, images, and other inputs.
Human observers — Reviewers who know the field, watch live runs, and write notes on what the agent does.

Outputs

Capability claims — Statements about what the agent reliably does, each backed by a run that was watched.
Constraint claims — Statements about what the agent cannot do, or does unreliably, each with a reference to a run.
Trial report — A record of what was observed. It informs the go or no-go call and the shape of the rollout.

Steps (5)

Curate realistic tasks
Pick a small, varied task set that looks like real use: drafting documents, scheduling, summarising meetings, playing through a strategic game, and answering a multi-step research question. Avoid tasks the test set already covers.
Instrument the agent for observation
Record the full run: tool calls, the reasoning in between, retries, and timings. Observers cannot grade what they cannot see.
usesDecision Log Lineage Tracking
Run trials with human observation
Have people who know the field watch the agent work through each task end-to-end. Note where it stalls, loops, makes things up, succeeds, or surprises them.
Extract grounded capability and constraint claims
Turn what you saw into specific claims, each with a run reference. For example, 'the agent reliably drafts X when given Y', or 'the agent fails when the input contains Z'. Reject any claim that no logged run backs up.
Feed findings back into evals and rollout gating
Add the newly found failures to the regression test set. Use the confirmed capabilities and limits to set how much freedom the agent gets in the next rollout step.

Framework-specific instructions

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AI-generated for Agent Development Kit (ADK) (Google) — verify against official docs.

Principles

A trial produces claims about behaviour, not benchmark deltas. The unit of evidence is the watched run.
Real, open-ended tasks reveal failures that made-up tests miss by design.
Every claim must be tied to a specific logged run, or it does not ship.
Findings feed both the test set and the rollout gate. A trial that updates neither was wasted.

Real-World Agent Trial

Methodology process overview

Steps (5)

Curate realistic tasks

Instrument the agent for observation

Run trials with human observation

Extract grounded capability and constraint claims

Feed findings back into evals and rollout gating

Framework-specific instructions

Principles

Known failure modes (2)

Related patterns (4)

Related compositions (2)

Related methodologies (2)

Sources (2)

Provenance