Choose your first GTM agent workflow by finding a repetitive job with clear inputs, a useful output, and a person who can judge the result. Measure the work before automating it. Start with a bounded task such as account research, then expand when the evidence supports it.
A demo can make a complicated system look compelling. The more useful test is whether it helps a real person do the job next week. That means deciding what the work is, what good looks like, and where a person keeps responsibility.
Choose the job before the agent
Write the workflow as a sentence: when this input arrives, someone uses this information to produce this output for this purpose.
For account research, that could be: when a target account enters the review queue, research its business and produce a short brief so a salesperson can decide whether and how to approach it.
That description makes the boundaries visible. The research system is preparing a decision. It is not deciding the entire sales strategy or starting a conversation with the account on its own.
Sometimes a defined workflow with a few model calls will handle the job. Sometimes the task needs more flexible tool use. Anthropic's guidance on building effective agents makes that distinction and recommends starting with the simplest approach that works. The architecture should follow the task.
Which workflows are good candidates?
Look for work the team repeats often enough to evaluate. It should have accessible inputs and an output that someone already needs. The reviewer should be able to tell whether the result is useful without repeating the entire task.
Good candidates for an initial review include:
- Account briefs: gather relevant facts and sources before a rep opens the account.
- ICP research: organize evidence against an agreed set of fit criteria.
- Outreach preparation: draft a note from an approved brief and a defined message.
- Support triage: identify the issue, retrieve relevant context, and prepare a draft response.
The right first choice depends on your bottleneck. If account selection is unclear, producing more outreach drafts may create more review work. If the team cannot access reliable account data, start by fixing that input.
A practical selection test
Choose the smallest recurring job where a better first draft would make the next person's work easier, and where the cost of a mistake can be contained during evaluation.
Define a useful output
Agree the output before choosing the tooling. For an account brief, I would want a consistent structure: what the company does, evidence of fit, relevant context, source links, open questions, and a recommendation for the human reviewer.
Make room for uncertainty. If the system cannot verify an important fact, it should say what is missing. A fluent paragraph with no way to check its claims makes review harder.
Include examples of an acceptable brief and one that needs more work. Explain why. That gives the operator a reference for reviewing results and gives the implementation a clearer target than “do good research.”
The same principle applies to an outreach draft. Specify the audience, the approved claim, the purpose of the note, and the information it may use. A draft should make the review decision easier.
Measure the current work and the review
Take a small, representative set of tasks and record how the team handles them today. Use the same examples to evaluate the first version of the workflow. Include difficult cases, incomplete inputs, and accounts that should be rejected.
| Measure | What it tells you |
|---|---|
| Preparation time | How much work is needed before review starts |
| Review and correction time | How much effort the output creates for the operator |
| Evidence quality | Whether important claims can be checked against sources |
| Usable outputs | How often the brief is ready for the next step |
| Running cost | What a usable result costs to produce and maintain |
Compare the complete process. If preparation gets faster but review becomes much slower, the team may not have gained anything. Record failures and corrections as carefully as successful examples. They tell you where the boundary or the inputs need work.
Put the human handoff in the workflow
My AI GTM operating model keeps a person on anything outbound. Research, briefs, and drafts can move through preparation steps. First touches, follow-ups, and replies wait for a person's decision before they leave the business.
Make the handoff explicit. The reviewer needs to see the proposed action, the evidence behind it, and anything that remains uncertain. Define who can approve the action and what happens if the work is rejected or needs another pass.
This affects the design of the system. An approval is an actual step with an owner and a record. It should be possible to inspect what was prepared, what the person changed, and what was eventually sent.
Give the workflow shared context
Account research, outreach, and support become disconnected when they each start from a different version of the business. Write down the ICP, positioning, approved claims, voice, and relevant account history. Identify which source owns each piece of information.
The objective is consistent work. If a support conversation changes the account's context, the person preparing outreach should be able to see that. If positioning changes, the workflow should stop relying on an obsolete example.
Start with the information the first workflow needs. Define access around that scope, and give someone responsibility for maintaining the source material. A larger store of documents does not help if the operator cannot tell which ones are current.
Adoption belongs in this design work, too. Have the people doing the job try the workflow, show where it creates extra effort, and help shape the handoff. The same questions apply in support, engineering, and leadership: who uses the output, what judgment stays with them, and what changes in their actual day?
Expand when the workflow earns it
Once the first workflow produces useful results on ordinary and difficult cases, decide what the evidence justifies. You might improve the input data, add another preparation step, or handle a second workflow with the same review process.
Keep an owner, a way to stop the workflow, and a manual fallback. Document what the system handles, where it stops, and which changes need another evaluation.
A useful first deployment gives the team a working process and a way to judge it. That is the starting point for my AI GTM audit and build sprint: choose the job, define the output, and put a small amount of useful work into production with the handoff understood.
Sources and context
- Anthropic: building effective agents, published December 19, 2024. Cited here for the distinction between defined workflows and agents, and the principle of starting simply. Its tooling discussion is not a current product comparison.
- Matt Newbill: AI GTM & Agent Ops. The research brief and scorecard above are proposed implementation examples, not client performance results.