A day out with Grandma

“Plan a rainy day out with Grandma. She can’t use stairs.”

The venue’s old guide says every gallery is accessible. A visitor says the lift is closed. An agent needs to investigate that disagreement before recommending a visit. AgentRun makes the research, judgment, follow-up, and writing separate steps you can inspect and test.

This example uses fictional sources and prewritten AI answers. The interpreter, routing, parallel execution, verification callbacks, and handoff are real. It makes no network calls and books nothing. It demonstrates the workflow, not model accuracy or real venue accessibility.

Run it

From the source directory, with Node 22.19+:

npm ci
npm run build
node examples/day-out.mjs repair
node examples/day-out-primer.mjs
node --test examples/day-out.test.mjs

The complete example contains the DSL, source pack, adapters, and CLI. The primer isolates the classifier and shows sift/pick. Tests check the boundaries and the complete runs.

Follow one run

1. Route the request. Jev distinguishes one access question from planning the whole day.
2. Research in parallel. One agent reads venue records; another reads the transport information. Each returns claims, exact quotations, source IDs, and missing topics.
3. Judge the evidence. The same classifier returns supported, conflicting, or missing using a complete rubric covering every required topic.
4. Investigate a conflict. A follow-up agent reconciles the claims. The current notice explicitly replaces the old guide. It confirms a step-free ground floor, but the lift is still closed. A recent-looking page alone would not establish authority.
5. Write and review. The first draft promises access to the whole museum. Its source ID and quotation are real, but its meaning overstates the evidence. Semantic review rejects it. The writer submits a qualified ground-floor plan within a two-submission limit.
6. Check provenance. Code confirms that source IDs exist and quotations occur in their sources. This catches invented citations; it does not prove that the writer interpreted a source correctly.

The final plan preserves the outdoor walk from the bus stop. An indoor museum does not make the journey rain-proof. It recommends confirming current arrangements before leaving.

Try the other paths

Command	What it shows
node examples/day-out.mjs ready	Consistent findings go directly to a qualified plan.
node examples/day-out.mjs repair	A source conflict triggers investigation, then an overstated draft gets corrected.
node examples/day-out.mjs quick	One access question skips the parallel venue/travel team.
node examples/day-out.mjs missing	An events leaflet does not establish access. The workflow asks a person before writing a recommendation.

Add --json to inspect inputs, calls, review submissions, and interpreter events.

Test one part

The primer copies the actual classifier node and its entire output schema/rubric into a small workflow. It runs saved findings with expected answers: supported, conflicting, and missing. A deliberately wrong answer produces a failed evaluation even though execution completes normally.

These tests establish the wiring of the evaluation, not the quality of a live model. To measure that, supply your real judge, build a representative evidence set, and retain expected outcomes. Then rerun the whole workflow to check interactions and failure paths.

Sift, then pick

Four venue descriptions become a shortlist. Jev keeps an indoor, step-free museum visit and an aquarium; a stairs-only garden and a hall with unknown access do not pass. Pick chooses the museum for someone interested in local history, keeping the ground-floor-only qualification. If no supplied option fits, it can return no choice.

The primer includes small individual workflows and a composed selectionWorkflow. These are separate from the research graph: you can reuse the same primitives in different arrangements.

Connect your agent

The source pack stays fictional in runDayOut(), including in host mode. You can first test a real host against those known documents:

import { runDayOut } from './examples/day-out.mjs';
import { createJevRunner } from '@agentrun/jev';

// Implement runNode in your host, using the contract below.
const run = await runDayOut('repair', {
  runNode,
  runJudge: createJevRunner(),
});

This is an integration outline: runNode is supplied by your application. Configure authorized TypeSafe access on the server before using the live judge; see Jev setup. The helper requires both adapters so a partly scripted run cannot quietly appear live.

runNode receives the assembled instructions, a JSON user input, an output schema, and—for the writer—a review(candidate) callback. Give the step an agent session and only the tools it needs. If review rejects a draft, pass its feedback to the same session; return the accepted result. Observe the cancellation signal and the interpreter’s declared review limit. The Pi SDK adapter implements this boundary for Pi.

To use your own documents, call runWorkflow(workflow, input, adapters) with the exported workflow and a matching input. runJudge owns live semantic judgments; agents own investigation and writing. The .facts fields are prewritten extraction answers used only by the offline adapter. Remove those fixture annotations when supplying real source documents.

Your outer agent can expose that call as a tool, such as plan_day_out. It receives the workflow’s structured result and can continue its own loop. The DSL does not automatically convert an arbitrary agent loop into a graph.

What passing means

A complete run passed its declared contracts and judgments. A bad reviewer can still approve an unsupported claim; a test explicitly demonstrates that limit. Citation checks, retry limits, and handoff paths make behavior inspectable, but they do not prove real-world truth or provide a security sandbox. Your host controls tools, accounts, effects, and deployment.