Prompt Kit
The 4:1 Ratio — Where to Spend Money vs. Engineering Time on Agent Deployment
Prompt Kit: The 4:1 Ratio — Where to Spend Money vs. Engineering Time on Agent Deployment
This kit operationalizes the two core frameworks from the article: the 4:1 ratio (four engineering problems your team can solve, one domain expertise problem where you probably need help) and the build-or-buy diagnostic (codebase readiness × organizational readiness × domain complexity). Four prompts, each designed for a different decision point — whether you're a VP evaluating a seven-figure consulting deal or an engineer who wants to make the codebase agent-ready before anyone asks.
How to use this kit
Prompt 1 is the starting point for organizations. Run it in any thinking-capable model (ChatGPT, Claude, Gemini). It scores your three variables and tells you where to route budget. Prompt 2 is for engineering teams — it audits your codebase against the eight-pillar readiness framework and generates a prioritized fix list measured in days, not quarters. Prompt 3 is for anyone evaluating a consulting proposal — it separates commodity engineering work (that you shouldn't pay for) from genuine domain expertise (that you should). Prompt 4 is for individual contributors — it maps the five hard problems to your specific situation and tells you exactly where your effort compounds.
Be honest with the inputs. These prompts are designed to cut through the noise, not reinforce the decision you already want to make. The frameworks are only useful if you score yourself accurately.
Prompt 1: Build-or-Buy Diagnostic
Job: Scores your organization across codebase readiness, organizational readiness, and domain complexity — then routes you to a specific build-or-buy recommendation based on the article's framework.
When to use: Before signing any consulting engagement. Before allocating budget for agent deployment. Before your next leadership meeting where "AI agents" is on the agenda.
What you'll get: A scored assessment across three dimensions, a clear routing decision (build it yourself / buy org-layer help only / buy domain expertise / fix the foundation first), estimated cost differential between paths, and a list of specific questions to ask any vendor or consultant.
What the AI will ask you: Your industry and regulatory environment, what agents would do in your org, your team's engineering capabilities, your org's history with technology-driven change, and whether you're currently evaluating consulting proposals.
Prompt 2: Codebase Agent-Readiness Audit
Job: Walks through Factory.ai's eight-pillar framework and generates a prioritized, time-estimated action plan to make your codebase agent-ready — the single highest-leverage thing an engineering team can do before deploying agents.
When to use: Before any agent deployment. Before evaluating agent platforms. When your team is arguing about which agent framework to adopt and nobody has checked whether the codebase can support any of them.
What you'll get: A scored assessment across eight pillars, a prioritized fix list with time estimates (days, not months), a draft AGENTS.md structure, and specific lint rules to implement as architecture enforcement.
What the AI will ask you: Your tech stack, repo structure, current CI/CD setup, testing practices, documentation state, and the most common "ask a human" moments in your development workflow.
Prompt 3: Consulting Proposal Decomposer
Job: Takes any AI/agent consulting proposal, pitch, or SOW and separates it into commodity engineering work (your team can do this with open-source tooling) vs. genuine domain expertise (worth paying for) — then tells you what you're actually being charged for.
When to use: When a consulting firm has pitched you on agent deployment. When your leadership is about to sign a six- or seven-figure engagement. When you need to walk into a meeting with a clear breakdown of what's worth buying and what's not.
What you'll get: A line-by-line decomposition of the proposal into "build" (your team) vs. "buy" (outside expertise), estimated cost of the build-it-yourself portion, specific questions to challenge each line item, and a counter-proposal structure.
What the AI will ask you: The consulting proposal details (scope, deliverables, pricing), your team's current capabilities, and your regulatory/domain context.
Prompt 4: Your Personal 4:1 Map
Job: Maps the five hard problems in agent deployment to your specific role, team, and situation — then tells you exactly where your effort compounds and where you should stop trying to solve it yourself.
When to use: When you're an individual engineer, tech lead, or small-team leader who wants to be the person who makes agents work in your org. When you need to know where to focus your time for maximum impact. When the article's framework resonated but you need it translated to your Monday morning.
What you'll get: A personalized map of the five problems rated by relevance to your context, a prioritized action list you can start without asking permission, specific deliverables that demonstrate value to leadership, and a clear line between "your job" and "not your job."
What the AI will ask you: Your role, your tech stack, what agents would do in your context, what your codebase looks like today, and what regulatory constraints (if any) apply.