AI
AI Agents Are Not a GTM Strategy
AI agents are useful for bounded, high volume, low ambiguity revenue tasks. They are not a substitute for an offer, a target market or a functioning system. How to scope them so they survive production.
- Author
- Felipe SozaFounder, GTM Systems Architect
- Published
- Updated
- Last reviewed
Executive summary
- Agents amplify an existing system. They do not create one.
- Scope, logging, review gates and unit economics separate production agents from demos.
- If a rule solves it, use the rule.
Key thesis
An AI agent is a program with judgment and a budget. Scope it like software, measure it like a worker, and never let it decide something you cannot audit.
The category error
Strategy is a set of choices about who you serve, what you offer and why it is better for them. An agent is an execution mechanism. Substituting the second for the first produces very fast, very consistent failure.
The teams getting real value from agents already had a working motion. They used automation to remove a specific cost, usually research time or triage time, and they measured the difference.
Where agents actually earn their keep
- Account research: turning unstructured public information into structured fields.
- Reply triage: classifying intent and objection type, routing, updating suppression.
- CRM hygiene: detecting duplicates, missing required data and stage violations.
- List QA: flagging records that fail ICP rules before they reach a sequence.
- Internal reporting: drafting the commentary around numbers a human then checks.
A practical framework: the five questions
- 01Scope. Can you describe the task in one sentence with defined inputs and outputs? If not, it is not ready.
- 02Baseline. What does a human cost per unit today, in time and quality? Without this, savings are imaginary.
- 03Evaluation. What is the scored test set, and what score is acceptable to ship?
- 04Blast radius. What is the worst thing a wrong output can do, and is that action reversible?
- 05Ownership. Who is paged when it misbehaves, and where is the kill switch?
Anything that fails question one or four should stay a proposal engine with a human committing the change.
Common failure modes
- One agent assigned five responsibilities, impossible to debug.
- Write access to the CRM before accuracy was ever measured.
- No logs, so failures are anecdotes.
- No cost ceiling per record, so a retry loop becomes a bill.
- Prompt changes shipped without evaluation, which is a regression waiting to be discovered by a customer.
The honest version of the pitch
Agents let a small team behave like a larger one on the tasks that are mechanical. They do not give you judgment, relationships or a reason to be chosen. Buy leverage, not narrative.