AI is everywhere in the post-sale conversation right now. Every Customer Success leader I speak with is being asked some version of the same question: “What are we actually doing with AI?”
For most teams, the honest answer is still, “We’re experimenting.” And that’s not a failure, it’s reality.
The problem is that experimentation without structure quickly turns into confusion, stalled momentum, or tools looking for problems to solve. What post-sale leaders need is not more hype or more platforms. They need a practical way to design an AI agent, define what an AI agent should do, why it exists, and how it creates value before anything gets built. That is exactly what an Agent Workflow Requirements Document is designed to do.
Designing Your First AI Agent: Agent Workflow Requirements Document
At its core, an Agent Workflow Requirements Document is a way to externalize judgment.
Instead of relying on people to constantly check systems, remember edge cases, and manually synthesize information, an agent can watch for specific situations, apply simple reasoning, and produce a useful output. The requirements document is not a technical specification. It is a business definition of delegated decision-making. Think of it as answering four simple questions:
- Why does this agent exist?
- When should it run?
- How should it decide what matters?
- What should happen as a result?
If you can answer those four questions clearly, you can design an agent.
Why Post-Sale Teams Struggle to Get Started with Agents
Most Customer Success, professional services, and post-sale leaders do not struggle because they lack ideas. They struggle because they jump too quickly to implementation. They ask:
- What tool should we use?
- Should this live in Salesforce?
- Do we need engineering?
- Is this safe?
- Will this replace people?
Those questions matter, but they come after clarity of intent. Without a structured way to define the agent’s purpose and behavior, teams either over-engineer or never start at all. The Agent Workflow Requirements Document is designed to sit before tools, platforms, and automation.
The Mindset Shift: From Automation to Judgment
One of the biggest mistakes teams make is thinking agents are just automation. Here’s the key difference: automation follows rules, agents apply judgment. In post-sale, much of the work is not binary. It is about:
- Deciding what information matters
- Identifying patterns across conversations
- Summarizing context for the next human decision
- Making sure nothing important falls through the cracks
That is why the document focuses so heavily on decision logic, not just triggers and actions. Decision logic does not have to mean scoring models or thresholds. In many first agents, it simply means deciding:
- What to include
- What to ignore
- What to summarize
- Who needs to see it
The Four Sections That Matter Most When Designing Your First AI Agent
To keep things simple and usable, the template focuses on four core sections.
- Agent Overview: This anchors intent. It defines the business objective, who the agent is for, and who owns it. If this is not clear, nothing else matters.
- Triggers: Triggers define when the agent wakes up. Good triggers align to real business moments, not just system activity. “Deal Closed Won” is a good trigger. “Field updated” usually is not.
- Decision Logic: This is the heart of the agent. It defines how information should be evaluated, selected, or summarized. This is where leadership judgment gets translated into something repeatable.
- Actions and Outputs: This defines what the agent produces and where it goes. Good agents create clarity and momentum, not noise.
You will notice what is missing: complex integrations, governance debates, and advanced metrics. Those come later. First agents should be simple, safe, and easy to evaluate.
A Concrete Example: The Sales Handoff Summary Agent
One of the easiest first agents for post-sale teams is a Sales Handoff Summary Agent. When a deal closes, this agent:
- Gathers deal context from a CRM like Salesforce
- Summarizes key sales conversations from tools like Gong
- Adds lightweight company context
- Produces a clean handoff summary for the post-sale team
This agent does not contact customers, change data, or make decisions for people. It prepares humans to do better work, which is the pattern you want for your first agent.
What Happens After You Fill Out the Document?
Once the Agent Workflow Requirements Document is complete, you have options. You can:
- Hand it to a CS Ops, RevOps, or technical partner to implement
- Use it directly as structured input to a low-code or agent-building tool
- Run a small pilot manually to validate the output before automating
The key is that the document removes ambiguity and gives everyone a shared understanding of what the agent is supposed to do.
Why This Approach Works
This framework works because it:
- Lowers the barrier to getting started
- Keeps leaders focused on outcomes, not tools
- Encourages experimentation without risk
- Scales judgment instead of replacing people
Most importantly, it reinforces a critical truth: You do not need to get your first agent right, you just need to get it started.
Design Your First Agent
If you are a post-sale or Customer Success leader trying to move from AI conversation to AI execution, start with structure.
👉 Download the Agent Workflow Requirements templates below:
- Instruction Guide
- Blank Agent Workflow Requirements Document
- Sample Completed Agent Workflow Requirements
If you use it, I encourage you to connect with me as would love to hear which workflow you design first. The leaders who build capability now will define how AI actually operates inside post-sale over the next three to five years.
