Case study 01
Small agents. Deterministic gates. Reviewable delivery.
Instead of loading every tool and rule into one agent, Pi routes repeatable work through CLI recipes, gives focused agents only the context they need, and requires isolated validation before QA handoff.
scope
Scope packet
gates
Delivery gates
route · isolate · validate · review
qa + receipt
QA controls
Receipt
Fast proof
A Pi-style local control plane keeps agentic delivery small, token-aware, isolated, and reviewable.
CLI-first boundary
Repeatable work stays in commands, recipes, and validators before any agent spends tokens.
Context economy
Agents get only the task, cookbook, repo profile, and optional code-graph context they need — scoped context keeps output cheap to produce and fast to review.
Contained failures
Worktree isolation, sandboxing, validation, and QA keep fast agent output reviewable.
Problem
A single bloated coding agent can move quickly but becomes expensive to review: too much prompt context, too many tools, weak isolation, and no compact proof that validation or QA happened.
What it proves
Jeff can turn agentic development from a single bloated assistant into a controlled local delivery loop with explicit policy, isolation, validation, and QA evidence.
Reliability pattern
Route approved specs into isolated worktrees, then require deterministic validation and QA evidence before delivery is presented as reviewable.
Sanitized evidence
This evidence is a reviewed sanitized snapshot of the delivery model, not raw transcript exposure. It demonstrates AI-assisted delivery reliability through the contract between task intent, isolated execution, deterministic validation, and QA handoff.
The publishable proof is the shape of the system: bounded task inputs enter an isolated work scope, validation records command-level results, and the handoff summarizes what changed, what was checked, and what still needs human review. Private paths, tickets, hostnames, raw logs, screenshots, and assistant transcripts stay out of the public case study.
Privacy notes
This case study uses the Pi PRD as authoring context and publishes only sanitized architecture language. It does not expose private repositories, hostnames, tickets, transcripts, tokens, employers, or clients.
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