GTM AI agents: how to build AI agents for your go-to-market team

The short answer. GTM AI agents are how AI for GTM gets work done: to build AI agents for your go-to-market team, give each one a job, a person it reports to, written rules, skills that run when something happens, and every customer conversation to read, then decide what it does alone and what a rep approves. Day AI gives every rep agents built this way, in place of their CRM or on top of Salesforce or HubSpot, working from a GTM context graph and governed by an agent control plane. Start each rep with two: one that keeps the pipeline current from their calls and email, and one that tells them what to work on next and drafts it.

Say you run revenue operations at an audit-software company with about 23 AEs, and it's 4:50 on a Thursday. A rep's discovery call has just ended. Before you switched, a home-built n8n workflow read each call on its own and sometimes updated the wrong deal. Now the rep's agent in Day AI reads the call against everything else on the deal, proposes new MEDDPICC scores, and attaches the people who were on the call to the right deal. The rep reads the proposal, approves it, and the scores are in HubSpot. Nobody built a webhook. People you've sent Day AI pages to have started asking you to set them up too, and the new reps who inherit accounts get up to speed on them from the same record. The agent did the typing, and the rep made the call.

Where the usual answer goes wrong. Most guides to GTM AI agents start with the workflow: pick a task, define "triggers, routing logic, confidence thresholds, output formats, and fallback paths" (Highspot, updated October 2026), and keep a human in the loop. That gets you one bot for the whole team, reading the CRM, which holds only what someone remembered to type. Day AI's view is that the conversations are the record and agents do the work while people approve it, so an agent should be designed like a hire who works for one person from every conversation, with rules the team shares. Day AI provides the context graph and agent control plane to quickly and securely upgrade your company brain. You may already have a folder of sales-process docs and a few Claude skills, and that was the right start.

The hire model for GTM AI agents, with Day AI

In engineering, one shared set of agents can serve the whole team. Sales doesn't work like that: each rep runs a different book of business, and each one answers for a number. So a GTM AI agent works for one person, while the rules it follows are shared. Christopher O'Donnell, Day AI's CEO, puts the design question this way: "Don't ask what software you'd buy, ask who you'd hire" (watch at 4:08). The hire model has six steps:

1. Pick the number it should move. "We built an agent" isn't an outcome. Pick a number from the sales plan, such as deals each rep actively works, hours of data entry a week or follow-ups sent within a day of a call, and write down today's baseline. In Day AI, put the number in the agent's job description, so it reads it on every run.
2. Write the job description. Say what the agent owns, what's out of scope and who it reports to, the way you'd onboard a new hire. In Day AI, every agent has a Job Description field written in the second person, and you can start from a template such as Sales Operator or CRM Data Entry Specialist.
3. Write the rules before the skills. The rules the agent must obey, such as when a deal may change stage and on what evidence, belong in a document it reads every time, not in someone's head. In Day AI, the rules go into the skill's instructions, and an admin can make the skill Managed so no rep can edit them.
4. Break the job into skills with triggers. Each repeatable part of the job is a skill that runs on a schedule, when something happens, or when the rep asks. In Day AI, a skill can run on a schedule, on a "Meeting summary" or "Email summary" trigger, or on request, and you can create one by asking your agent in chat.
5. Decide what it does alone and what waits for a person. List what the agent may change on its own, what it sends to the rep to approve, and what it never does. In Day AI, an agent inherits the permissions of the person it works for, and you can write a new skill to report what it would have done until you tell it to act, the way Day AI's Opportunity Automation skill starts in learning mode.
6. Read the work and coach it. Check a sample of its output every week against the calls it came from, and tell it what to change. In Day AI, every skill keeps its run history, and when you give feedback the agent updates the skill and confirms what it changed.

Most homemade GTM AI agents skip steps 1 and 3: they start from a task, and the rules live in a prompt nobody else can read.

How to build GTM AI agents in Day AI, step by step

The example is the one from the video on this page: Margo, a rep with 17 open deals, two or three high-value calls a day, and two 90-minute blocks on her calendar every week for typing what happened into Salesforce. The goal is to double her high-value calls and activities by taking that data entry off her calendar.

Step 1: Connect the conversations to Day AI

An agent is only as good as what it reads, so start with the GTM context graph. Each person connects Gmail and Google Calendar and sets their sharing rules, and a new person's first email and calendar sync takes one to six hours. Turn on the notetaker for the meetings you want recorded, connect Gong or Granola if your call history lives there, and add Day AI to the Slack channels that matter. If you're keeping Salesforce or HubSpot, each person connects them, with their own login, through their MCP servers, which allow reads and writes when the write tools are turned on.

Step 2: Create the agent from a template in Day AI

Create Margo's first agent from the CRM Data Entry Specialist template, which keeps people and company records current from every email and meeting and fills missing fields from email signatures, the public web and earlier conversations. For the pipeline itself, the Sales Operator template moves stages from what was said, files the next step on every open deal, flags deals without a real close date and sends a daily pipeline snapshot. Then edit the Job Description: who the agent works for, the number it moves, and what's out of scope.

Step 3: Write the stage rules into a skill in Day AI

Before the agent moves anything, write one spec for when a deal moves between stages and when it must not. Cross-reference your written sales process with how your pipeline is set up today, and favor rules that can be checked against a call or an email:

# Stage rules (v3)
## Discovery → Evaluation
Move only when a call or email shows all of:
- A named pain, in the buyer's words
- A second person from the buyer's side involved
- An agreed next meeting with a date
Never move on a rep's note alone. Cite the call or email that justifies the move.

In Day AI, ask your agent to create a skill that applies these rules, triggered by "Meeting summary" so it runs when each recorded meeting is summarized, and another on "Email summary" for email. Keep any field your sales leader wants reps to fill in by hand out of the skill, and have the agent email the rep a weekly summary of their pipeline data instead.

Step 4: Have it report before it acts in Day AI

An agent that moves deals should earn the right to act. Ask the agent to write the skill so it reports what it would have done first, the way Day AI's Opportunity Automation skill starts in learning mode, set up how it notifies you (email or a Slack channel), and read those reports for a week. When its proposals match what you'd have done, tell the agent it may start acting. Draw the lines that never move: a common one is that the agent creates deals and moves stages but stops before closed-won and closed-lost.

Step 5: Add the second agent in Day AI

Margo's second agent is a coach: a scheduled skill that sends her a ranked list through the day, with follow-up emails drafted for her to review, deals where something is slipping, and prep for her high-value calls built from what worked in similar deals. In Day AI, give it its own job description, run it on a schedule, and deliver it in Slack or email. The rep stays the face of the company, the person who listens, makes promises and stands behind them; the agents do the research, the recall and the record keeping around her.

Step 6: Deploy the agents to every rep in Day AI

Once one rep's agents work, add the skills to the library and choose which agents get them under Manage deployment. Make the stage-rules skill Managed, so every rep's agent runs the same version and nobody can edit the rules; only workspace admins can create Managed skills. Managed skills read workspace-shared data only, so share the meetings and email the rules depend on, or use a Template if the skill needs each rep's private email. Make the coaching skill a Template, so each rep adjusts how it writes and when it arrives. For the full playbook, read how to deploy the same skill to every rep's AI agent.

Where homemade GTM AI agents break, and what Day AI does

A single person's agents can run on a laptop with a cron job. Problems appear as the team relies on them.

• One bot for everyone. The common first build is one assistant in Slack that everyone asks questions. It has no owner and no job, and it can't remember that one rep wants the brief at 7:00 and another wants it shorter. In Day AI, each rep has their own agent that takes coaching, while the team's rules are shared.
• Reps babysit the bot. "Tell Claude what to put in Salesforce, wait, then check Salesforce" is slower than typing it in. In Day AI, skills run when a meeting is summarized or an email arrives, so the update happens without the rep asking.
• Work done in a chat disappears. A rep spends an afternoon getting an accurate pipeline report out of a chat thread, and the next rep starts from zero. In Day AI, the rep asks the agent to turn that work into a skill, and an admin can deploy it to the team.
• Triggers are plumbing. "Every time a meeting ends, do this" means webhooks, queues and a runner acting as each rep. It works for one person and becomes a project for fifteen. In Day AI, a trigger is a setting on the skill.
• The agent sees too much. An agent on one service account reads every inbox it can reach. In Day AI, each agent works within the permissions of the person it reports to, and each person's sharing rules decide what's shared.
• Nobody can say why a field changed. When a script writes the CRM, a wrong value has no trail back to the call. In Day AI, every value shows its sources, and you can ask the agent what changed and why.

To score the agents you already have, use how to grade your company's AI agent setup. GTM AI agents are one half of AI for GTM; the GTM context graph they read is the other. The AI for GTM guide covers both, with a 30-day rollout. Day AI is priced per agent, with no usage-based pricing; see pricing.