Robert Helewka
fd622112f4
- Add Quentin as work-team lead agent for solution architecture & demos, with aws_sa repositioned as Quentin's exclusive subagent (tool, not peer) - Add CASE as engineering lead agent for field/physical layer (LAN, hardware) - Bump unified Neo4j schema to v2.4.0: - Add Solution and Demo node types under Quentin's domain - Update assistant ownership table with Quentin, CASE, and AWS SA scoping - Update README and neo4j shared docs to reflect new team rosters and subagent boundaries
101 lines
7.1 KiB
Markdown
101 lines
7.1 KiB
Markdown
# Work Subagents
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The work leads (Alan, Ann, Jeffrey, Jarvis, Quentin) delegate narrow specialist 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. **Quentin** drives the architecture-focused subagents (aws-sa exclusively, plus tech_research).
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Subagents are runtime processes exposed as MCP tools. The canonical prompt text lives in `prompts/work/subagents/` — copies in the runtime code should match.
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## Catalog
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### research
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**Purpose:** Answer a question by querying both the public web and Robert's Neo4j memory in parallel, then synthesizing one integrated response with conflicts flagged and suggested memory updates surfaced.
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**Composition:** `fast.parallel` of three sub-agents:
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- `web_search` — argos
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- `memory_lookup` — neo4j (read-only); framed around work-team node types (clients, opportunities, engagements, decisions, technologies, contacts)
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- `synthesizer` — merges the two reports, flags conflicts, suggests which node type a memory update would belong on
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**Tools:** argos, neo4j_cypher
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**When to delegate:**
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- Pre-meeting prep on a client or contact — what's already in the graph, plus current public information (recent news, funding, leadership changes)
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- Opportunity qualification where the answer might exist in prior `Decision` or `Technology` nodes AND on the public web
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- "What do we already know about X, and what's the current public information on it?" against the work pipeline
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- When a lead wants memory-aware research without burning its own context on parallel queries
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**When NOT to delegate:**
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- Quick web lookups where memory isn't relevant — use argos directly
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- Pure graph queries on a known client or opportunity — query Neo4j directly
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- AWS architecture design questions — use `aws-sa`
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- Deep library/framework/API research — use the work-team `tech_research` subagent (below).
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**Prompt:** [prompts/work/subagents/research.md](../../prompts/work/subagents/research.md)
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**Runtime:** TBD — copy of engineering's `kottos/agents/research.py` with the work-team memory framing applied. Port to be assigned when wired up.
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---
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### aws-sa
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**Driven by:** Quentin (exclusive). AWS architecture requests from other work leads route to Quentin, who delegates to aws-sa. Demo-scoped — demo-scale designs only, no production accounts/data/hardening; production architecture routes to Scotty via Quentin.
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**Purpose:** AWS cloud architecture design. Selects services, defines how they connect, evaluates trade-offs, estimates costs, and produces architecture diagrams as SVG. Follows the AWS Well-Architected Framework across all six pillars.
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**Composition:** Single `fast.agent` with detailed instructions covering Well-Architected principles, SVG diagram production rules, and the requirements-then-design workflow.
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**Tools:** `aws-knowledge` (primary), `aws-docs` (API reference fallback), `aws-pricing` (real cost estimates), `argos` (web research), `context7` (library docs)
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**When to delegate:**
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- A client engagement requires AWS architecture design — service selection, network topology, cost estimation, multi-region considerations
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- Robert's own infrastructure needs AWS design work (rare, since most of his lab is Incus/on-prem)
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- Architecture review of a proposed AWS design — does the pillar trade-off math actually hold up?
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- Any time a current AWS pricing or service-availability answer is needed (don't guess from training data)
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**When NOT to delegate:**
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- Implementation work — Terraform, CDK, CloudFormation, CLI commands. aws-sa is design-only. For implementation route to Scotty (operate) or Harper (build).
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- Non-AWS cloud architecture — aws-sa is AWS-specific. Other clouds would need their own subagents.
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- General "what cloud service should I use" questions where the answer is obvious. Use aws-sa when the design genuinely needs the Well-Architected discipline.
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- Account-level operations (creating resources, modifying IAM, touching running infra). aws-sa recommends; it doesn't act.
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**Distinctive output:** SVG architecture diagrams. Every non-trivial design produces a diagram with explicit grouping (VPC, subnets, AZs, regions), labeled arrows showing data flow, and consistent AWS conventions (orange #FF9900 for service headers, dashed borders for groups).
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**Prompt:** [prompts/work/subagents/aws-sa.md](../../prompts/work/subagents/aws-sa.md)
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---
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### tech_research
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**Driven by:** Quentin primarily (technical investigation feeding solution design and demos), available to any work lead.
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**Purpose:** Investigate technical questions — library/framework/API comparisons, documentation, real-world code examples — and return structured analysis with cited recommendations.
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**Composition:** Single `fast.agent`. Checks **context7** (official docs) → **github** (real-world code) → **argos** (web fallback), adapting order to the query.
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**Tools:** `context7` (primary), `github`, `argos`
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**When to delegate:**
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- Library/framework/API comparison for a solution's stack or a demo (non-AWS technical depth)
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- "What's the current best way to do X with library Y?" with version-compatibility notes
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- Documentation and real-world code examples to ground a design decision
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**When NOT to delegate:**
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- AWS architecture questions — use `aws-sa` (it has the AWS knowledge/docs/pricing servers)
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- Quick tactical web checks — use `argos` directly
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- Memory-aware research blending the graph and the public web — use `research`
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**Note:** Work-team copy of engineering's `tech_research`, per the cross-team-reuse convention (copy with tweaks, don't share a file). The prompt is domain-neutral, so the copy is near-identical.
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**Prompt:** [prompts/work/subagents/tech_research.md](../../prompts/work/subagents/tech_research.md)
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---
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## Conventions
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**Source of truth:** koios is the master. The prompt text in `prompts/work/subagents/` is canonical; runtime `.py` files (when wired up) should load from or match these prompts. When iterating, edit koios first and propagate.
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**Personality:** Subagents have minimal personality. Their identity is their role — "you are an AWS Solution Architect," not a named character. The aws-sa prompt is longer than most subagent prompts because the role genuinely requires detailed guidance (Well-Architected pillars, SVG construction rules) — but it's still role-driven, not character-driven.
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**Cross-team reuse:** A subagent may be useful to other teams. The convention is **copy with tweaks** rather than share a single file — small per-team adjustments are legitimate and the duplication is cheap. aws-sa lives in `work/subagents/` because most AWS design work shows up in client engagements, but engineering could legitimately have its own copy if Robert's lab grew into AWS.
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**Graph ownership:** Subagents do not own node types and generally do not write to the graph. If a subagent's output needs to be persisted (an architecture decision, an opportunity-relevant cost estimate), the calling lead persists it. Architectural decisions belong on Alan's `Decision` nodes; technology evaluations linked to Robert's stack belong on `Technology` nodes.
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