Pick agent stack

Pick Agent Stack

Overview

pick-gtm-agent-pattern decides an agent's shape (pattern, tier, flowchart). This skill decides what runs it: the execution engine, LLM routing, evals, observability, state, secrets, and ingress — chosen per layer from the mapped core stack, sized to the agent's tier so a simple report agent doesn't get a ten-layer platform.

The recall dataset lives in ONE place — fetch it, never reproduce from memory:

https://labs.bowtiefunnel.com/tools/tools.json

Filter to category === "Agent Infrastructure" (core_stack: true) — the Headless GTM OS components. (Inside the bowtie-funnel-Labs repo, read docs/tools/tools.json directly.)

The layer map

Layers in pipeline order. An agent uses a layer only if it needs it:

#	Layer	Component
0	Secrets & config	Infisical
1	Code, review & CI quality gate	GitHub (+ DeepEval PR evals)
2	Durable execution	Trigger.dev
3	Webhook ingress & ACK buffer	Svix
4	Schema validation	Zod / Drizzle
5	PII / secret redaction	CloakPipe
6	LLM routing & fallback	Vercel AI Gateway → OpenRouter (tier-route locally with tools/llm-switchboard)
7	LLM observability & cost	Langfuse
8	State, storage & locks	Supabase
9	Human-in-the-loop interface	Slack

Steps

1. Start from the pattern verdict. If pick-gtm-agent-pattern hasn't run, run it first — the tier drives how much stack the agent earns:
   • Deterministic pipeline / report agent → layers 2, 6, 8 (+ 0 if it has any keys at all). No ingress, no evals, no redaction.
   • Guarded judgment (drafts, scoring) → add 1 (DeepEval gates), 4, 7.
   • Full decision loop / client-facing → add 3, 5, 9, per-client Infisical workspaces and Langfuse projects.
2. Fetch the dataset (curl -s https://labs.bowtiefunnel.com/tools/tools.json) and filter to Agent Infrastructure. It evolves with the stack; memory is stale.
3. Walk the layer map top to bottom. For each layer the agent needs, take the component and note its role in this agent. For each layer it doesn't need, record it in the layers NOT provisioned list with a one-line reason — that list is what stops platform creep.
4. Cross-check the known constraints:
   • Long-running LLM loops → Trigger.dev tasks, never 30-second serverless HTTP.
   • Slack as an interactive channel → Svix (or an edge function) must ACK within Slack's 3-second timeout, then hand off async.
   • Payloads > 3MB → pointer pattern: write to Supabase Storage, pass the path.
   • Concurrent tasks touching one record → Supabase Postgres advisory locks.
   • Client PII → CloakPipe before any LLM call; per-client Infisical + Langfuse.
5. Output the verdict: a per-layer stack table, the layers NOT provisioned, and the cost note — Infisical, Svix, and DeepEval all run $0 at build scale.

Common mistakes

• Ten layers for a tier-1 report agent — the layer map is a menu, not a checklist; most agents need three or four layers.
• Skipping DeepEval on prompt-heavy agents — if prompt regressions can break output quality, the PR gate is not optional.
• Recommending from training data instead of fetching the dataset — component choices and their roles evolve with the stack.
• Reaching for n8n for multi-engineer, code-first agents — visual canvases don't merge in git; n8n is for simple linear integrations by non-engineers.
• Raw scraped HTML or PDFs through task triggers — Trigger.dev payloads cap at 3MB; store first, pass the pointer.
• No Slack ACK buffer on interactive agents — a 4-second LLM call shows the user dispatch_failed; ingress must return HTTP 200 first.