Research Session Orchestrator

Run a full multi-source research session: classify the domain, dispatch parallel agents, synthesize findings, and output a formatted report.

When NOT To Use

• Drilling into one subtopic of an active session (use tome:dig)
• Merging findings already gathered (use tome:synthesize)

Workflow

Step 1: Classify Domain

Run the domain classifier on the topic:

python
from tome.scripts.domain_classifier import classify

result = classify(topic)
# result.domain, result.triz_depth, result.channel_weights

If confidence < 0.6 the classifier abstains and refines rather than rejecting: result.candidates lists the domains that had keyword support, triz_depth becomes the deepest of those candidates, and channel_weights is a support-weighted blend. Coverage widens on ambiguity instead of narrowing, because a topic spanning several vocabularies is exactly what the cross-domain channel is for.

Report the abstention to the user with the candidate list and let them override the domain. Do not treat a refined plan as a failure; treat it as the classifier declining to guess.

When candidates is empty the topic produced no keyword hits at all. That stays on the cheap two-channel plan, since there is nothing to refine toward and escalating noise wastes budget. If the topic is genuinely researchable, the vocabulary in _DOMAIN_KEYWORDS is missing it: say so rather than forcing a domain.

Step 2: Plan Research

python
from tome.scripts.research_planner import plan

research_plan = plan(result)
# research_plan.channels, research_plan.weights, research_plan.triz_depth

Step 3: Create Session

python
from tome.session import SessionManager

mgr = SessionManager(Path.cwd())
session = mgr.create(topic, result.domain, result.triz_depth, research_plan.channels)

Step 4: Dispatch Agents

Launch research agents in parallel using the Agent tool. Use this mapping:

| Channel | Agent Type | Prompt Includes | |---------|-----------|-----------------| | code | tome:code-searcher | topic | | discourse | tome:discourse-scanner | topic, domain, subreddits | | academic | tome:literature-reviewer | topic, domain | | triz | tome:triz-analyst | topic, domain, triz_depth |

Rules:

• Always dispatch code and discourse agents
• Dispatch academic agent only if "academic" is in

research_plan.channels

• Dispatch triz agent only if "triz" is in

research_plan.channels AND triz_depth != "light"

• Dispatch all eligible agents in a SINGLE message

(parallel, not sequential)

Each agent prompt must include:

1. The topic string
2. The domain classification
3. Any channel-specific context (subreddits for discourse,

triz_depth for triz)

1. Instruction to return findings as JSON

Step 5: Collect and Synthesize

After all agents return:

1. Parse each agent's findings into Finding objects
2. Record what each agent actually searched, before

merging anything:

python from tome.synthesis.quality import parse_envelope

for envelope in agent_envelopes: # one per dispatched agent session.query_log.extend(parse_envelope(envelope))

This is the step that makes an empty channel readable. Findings record what was found; the query log records what was looked for, and without it a channel that errored and a channel that searched a thin topic are the same thing: no findings. Skip this and every channel in the report reads unknown.

1. Merge using tome.synthesis.merger.merge_findings()
2. Rank using tome.synthesis.ranker.rank_findings()

Step 6: Generate Output

python
from tome.output.report import format_report, format_brief, format_transcript

# Default to report format
output = format_report(session)

# Save to docs/research/
output_path = f"docs/research/{session.id}-{slug}.md"

Save the session state:

python
mgr.save(session)

Step 7: Present Results

Display a brief summary to the user:

• The frontier verdict and its reason, from

tome.synthesis.frontier.frontier_verdict(session). It is the report's own answer to "did we find little because there is little, or because the search went badly"

• Number of findings per channel, with its outcome status

from tome.synthesis.quality.channel_outcomes(session): ok, empty, error, rate_limited, degraded, or unknown

• Top 3 findings by relevance
• Path to saved report
• Any research stories from

tome.synthesis.frontier.frontier_stories(session). Each is a gap with its evidence, and each arrives undecided. Ask the user to mark it act, defer, or decline. Do not decide for them, and do not file an issue for a story they have not marked: nothing in a search record says what is worth this project's time. On defer, file it with minister:create-issue so it survives the session. On act the work starts now and needs no issue. On decline record nothing.

The three retrieval channels run a positive control before their topic queries, so INCONCLUSIVE now means something specific rather than "controls do not exist yet". Read it as one of two things: a channel failed its canary and is blind, or a channel searched without running one. Both are named in the verdict's evidence, and both produce a story under Research Stories.

triz runs no control and is excluded from the verdict. It generates analogies rather than retrieving prior work, so its output is not evidence about what has been published and its findings are not counted toward coverage.

State plainly which channels did not return cleanly. A summary that reports "3 findings" without saying two channels were rate-limited invites the reader to treat a half-run search as a finding about the topic.

Then offer interactive refinement: "Use /tome:dig \"subtopic\" to explore specific areas."

Error Handling

• If an agent fails, continue with remaining agents
• If all agents fail, report the error and suggest

manual research approaches

• If synthesis produces 0 findings, state this clearly

rather than generating an empty report

• Save session state even on partial failure

Output Format Selection

| Flag | Format | Function | |------|--------|----------| | (default) | report | format_report() | | --format brief | brief | format_brief() | | --format transcript | transcript | format_transcript() |

Exit Criteria

• ] Domain classified before agents are dispatched; if confidence

< 0.6, user confirmation is requested before proceeding

• ] Code and discourse agents always dispatched; academic and triz

agents dispatched only when their channels are in the plan; all eligible agents sent in a single parallel message

• ] Session saved to docs/research/{session.id}-{slug}.md after

synthesis regardless of whether all agents succeeded

• ] Top 3 findings by relevance score displayed to the user with

the path to the saved report

• ] If all agents fail, error reported and manual alternatives

suggested; an empty report is never generated