# Engineering Subagents

The engineering leads (Harper, Scotty, CASE) delegate narrow, repeatable tasks to **subagents** — minimal-personality agents with a tight tool surface and a focused role. Subagents are called as tools, not addressed as collaborators. They don't own graph nodes and don't have character bibles.

Subagents are runtime processes (defined under `kottos/agents/`), exposed as MCP tools via StreamableHTTP. The canonical prompt text lives in `prompts/engineering/subagents/` — copies in the runtime code should match.

## Catalog

### research

**Purpose:** Answer a question by searching the public web, Robert's Neo4j memory, and the Mnemosyne document library in parallel — then judging what came back and verifying it against live pages where it matters.

**Composition:** `fast.parallel` of four sub-agents:
- `web_search` — argos; reports blocked/empty/JS-shell results rather than papering over them
- `memory_lookup` — neo4j (read-only)
- `doc_lookup` — mnemosyne (read-only); passes `library_type` when the domain is clear
- `synthesizer` — the researcher: reads all three reports, escalates to **dolores** for a real browser when a source is blocked, a URL looks guessed, or a load-bearing claim needs a live check

**Tools:** argos, neo4j_cypher, mnemosyne, dolores, time

**When to delegate:**
- A question where being wrong has a cost — the answer gets verified, not just retrieved
- "What do I already know about X, and what's actually true about it now?"
- Anything whose answer goes stale: prices, availability, dates, current status
- When the lead wants memory-aware research without burning its own context

**When NOT to delegate:**
- Quick web lookups where memory isn't relevant — use Argos directly
- Pure graph queries where the web isn't needed — query Neo4j directly
- Operating a browser as the goal itself (a form, a flow, a screenshot) — use `dolores`

**Prompt:** [prompts/engineering/subagents/research.md](../../prompts/engineering/subagents/research.md)

**Runtime:** `kottos/agents/research.py` — port 24150

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### dolores

**Purpose:** Operate a real web browser. Dolores drives a headed Chromium on the RDP desktop host via Playwright — she reads live pages, fills forms, works through multi-step flows, and returns screenshots.

**Composition:** single `fast.agent`.

**Tools:** playwright

**When to delegate:**
- A task that genuinely requires a browser: a form to fill, a login flow, a multi-step navigation
- A page Argos can't render — JS-heavy, client-rendered, cookie-walled
- When the *visual itself* is the deliverable ("what does this page look like")

**When NOT to delegate:**
- Finding an answer — that's `research`, which will call Dolores itself when a source needs verifying
- Anything the cached snippet already answers

**Sizing the task:** one page and one question at a time. She has no view of the caller's goal, so a task spanning several sites — or one whose later steps depend on what earlier ones turn up — comes back thin or wrong. Chain the steps from the calling side instead.

**Prompt:** [prompts/engineering/subagents/dolores.md](../../prompts/engineering/subagents/dolores.md)

**Runtime:** `kottos/agents/dolores.py` — port 24153

---

### tech_research

**Purpose:** Investigate technical questions — library comparisons, API docs, framework patterns, code examples. Returns structured analysis with options, trade-offs, code snippets, version notes, and cited recommendations.

**Tools:** context7 (primary), github, argos (fallback)

**When to delegate:**
- "How does library X work?" / "What are my options for Y?" / "Which framework should I use for Z?"
- Anything where the answer requires checking current documentation, real-world code, and possibly web research
- Library version migration questions
- API design comparison work

**When NOT to delegate:**
- General research where memory matters — use `research` instead
- Quick documentation lookup on a known library — use Context7 directly
- Code review of Robert's own code — leads handle that with their full context

**Prompt:** [prompts/engineering/subagents/tech_research.md](../../prompts/engineering/subagents/tech_research.md)

**Runtime:** `kottos/agents/tech_research.py` — port 24151

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## Conventions

**Source of truth:** koios is the master. The prompt text in `prompts/engineering/subagents/` is canonical; runtime `.py` files should load from or match these prompts. When iterating, edit koios first and propagate.

**Personality:** Subagents have minimal personality. Their identity is their role: "you are a technical research specialist," not a named character. CASE was once cataloged here but was promoted to a lead agent in 2026-05 — see [case.md](case.md). The line: if the agent has a character, an inspiration, a domain it owns end-to-end, it's a lead; if it's a narrow utility called by other agents, it's a subagent.

**Cross-team reuse:** A subagent may be useful to other teams (work, personal). The convention is **copy with tweaks** rather than share a single file — small per-team adjustments (different tool emphasis, different output format) are legitimate and the duplication is cheap.

**Graph ownership:** Subagents do not own node types and generally do not write to the graph. If a subagent needs to persist something, it returns the proposed write to the calling agent and lets the lead persist it.