A phased approach that takes someone from AI curious to confident operation. Five questions, asked in order: can we, should we, how would it work, how do we run it, how do we improve it.
The questions that are hardest to answer are often the ones that matter most.
If any answer is fuzzy, that's where to start.
Most agent projects don't fail in implementation. They fail because the requirements were never honest. These five questions surface the gaps before you spend money on them.
Fill in four fields and see which parts of this blueprint matter most for your situation. Takes about a minute.
Before designing or building anything, get clear on what the agent would actually do — and whether the technical pieces exist to make it work.
In one sentence, describe what success looks like when this agent is working perfectly. This sentence becomes the north star for every later decision. If you can't write it clearly, the agent isn't ready to be built.
If you can't write step-by-step instructions for someone you just hired, the agent can't do the work either. Workflow clarity is a prerequisite, not a deliverable — and it's the single biggest predictor of whether an agent will succeed.
In my experience, workflow mapping is where most SMB agent projects stall — not because the work is hard, but because no one's ever had to describe it step-by-step before. That friction is actually useful: it surfaces the decisions you've been making on autopilot, and those are exactly the decisions the agent will need rules for.
What information does the agent need to do its job? Map every source of data the agent will read or receive — including the things you currently keep in your head.
What does the agent produce, and where does the result end up?
AI agents change what people spend their time on. They rarely eliminate the need for people entirely. In a small business or solo practice, the same person frequently plays all of these roles — naming them anyway helps you see whether one person is being asked to do too much.
How will people interact with this agent — or will it run without direct human interaction?
What systems does the agent need to connect to? For each, assess how readily it can be automated.
Feasibility tells you that you can build an agent. The business case tells you whether you should. Not every viable agent is worth building.
Define at least one measurable outcome tied to business impact.
AI agents have ongoing costs: compute, maintenance, monitoring, and your attention. There must be enough task volume to justify those costs.
If a task happens often enough that you've built routines around it — daily, or many times a week — there's probably enough volume. Once-a-month tasks rarely justify the setup. The lower the volume, the higher the per-task value needs to be to make the math work.
One of the most important design inputs. Some tasks require the agent to be precise and deterministic — giving the same correct answer every time. Other tasks benefit from exploration and generation. Most tasks fall somewhere in between.
Where this agent falls on the accuracy-to-creativity spectrum directly affects how it should be built. More accuracy means more structured rules, validated sources, and systematic checks. More creativity means more model freedom and heavier reliance on human review.
Every agent will eventually make a mistake. The relevant questions are how easily mistakes can be detected and what happens if one is missed.
This is the question that most changes the architecture of an agent. High cost-of-error agents need more human-in-the-loop checkpoints, smaller autonomous steps, and more conservative autonomy levels — not because the AI is less capable, but because the asymmetry between "worked fine" and "caused a problem" is too large to ignore.
How quickly is this agent needed? What external deadlines exist? Timeline affects scope: a "good enough" agent in four weeks is often worth more than a perfect agent in six months.
Not everything needs an AI agent. Could this be solved with a simple automation, a workflow rule, an RPA bot, or a better template? What makes intelligence and adaptability necessary here? If you can't answer this clearly, you may not need an agent.
With feasibility confirmed and the business case justified, define how the agent should behave, what guardrails it needs, and who is accountable.
Most agents should start at Level 1 and earn their way to higher levels through demonstrated performance. Think of it like onboarding a new hire: you check the work closely at first and give more independence as they prove themselves.
Define what evidence would justify moving the agent up a level. This turns trust into a measurable process rather than a gut feeling.
Stop-and-ask criteria. Define the specific situations where the agent must pause and get explicit approval.
Agent owner. Every agent needs a named owner — a person accountable for the agent's behavior, outputs, and ongoing performance. Write the name down. Without explicit ownership, agents drift.
Task horizon cutoff. How long can the agent work on a single task before it should stop? An agent stuck in a loop can burn through compute and produce compounding errors. Set a reasonable time or token budget.
Lead with the practical question: what data does this touch, and who would be hurt if it leaked tomorrow?
Beyond human review, the agent itself needs built-in safety mechanisms that work even when no one is watching.
Technical identity and behavior settings.
What happens when the agent is unavailable or underperforming? Every production agent needs a fallback plan.
A deployed agent is not a finished product. It requires ongoing monitoring, cost management, and maintenance.
For SMBs, cost controls are often the most underestimated requirement. A single misbehaving agent running overnight can generate a surprising bill. Set hard daily caps from day one — even if they feel conservative. You can always raise them after you understand your actual usage patterns.
AI agents are not "set and forget." Data changes, models are updated, and business processes evolve.
Evaluation starts during development and continues throughout the agent's life. It's the difference between an agent that works in a demo and one that works in production.
A collection of real-world examples with verified correct answers. This is your objective measurement of whether the agent does its job correctly.
The hardest part of building AI agents isn't the technology. It's the requirements.
The fields in this framework that are most difficult to fill in are almost always the ones that matter most. If you can't articulate the agent's prime directive, if the workflow hasn't been mapped, if no one can define what a correct output looks like — those aren't reasons to skip ahead. They're reasons to pause and do the foundational work.
Agents that succeed in production share a common trait: the people who built them invested time to understand the work before trying to automate it. This framework is designed to structure that investment.
Use it iteratively. Revisit sections as you learn. Whether you're a solo operator or a growing team, the best agents come from the same thing — honest answers to hard questions, asked early.