Resource

🕵️‍♂️ The RevOps Guide to AI Agents

7/22/2025

by Sara McNamara

Here are my learnings in the AI Agent world so far, as a RevOps professional! I plan to continue to add learnings, as I go along in my own journey!

📖 Introduction 1. What is an AI Agent? 🤔2. Types of Ops AI Agents 🤖3. Best Use Cases to Start With (Marketing + RevOps) 🏗️🧼 List Import Review, Cleanse & Enrichment Agent 🔄 Daily Pipeline Review Bot 🧠 Lead Scoring + ICP Fit Checker 📈 Campaign Performance Watchdog 📬 Newsletter Drafting Bot 💼 Job Change Trigger Bot 4. Anatomy of an AI Agent (with MCP) 💀🔍 What's Inside an MCP (Model Context Protocol)?🎡 Example MCP for List QA Agent 🚧 Why MCPs Matter 5. Limitations of AI Agents 🙅‍♀️6. Tools to Build AI Agents 🤹‍♀️🙂‍↔️ Pro Tip: Use ChatGPT or Claude to Help Draft Your Agent Prompts & MCPs

📖 Introduction

AI agents are no longer futuristic ideas or experimental toys. They’re here, they’re powerful, and they’re reshaping how work gets done — especially in GTM, RevOps, and Marketing Ops.

But most people still don’t know where to start, what’s possible, or how to make agents reliable.

This guide is built for practitioners who want real-world use cases, tools, and frameworks to build agents that actually work inside their ops stack. I am not claiming to be the end-all resource on AI Agents, but I want to share my learnings with you.

1. What is an AI Agent? 🤔

An AI Agent is more than just ChatGPT with tasks. It's an autonomous worker that:

Has a goal (i.e. "Summarize open deals")

Can take actions (i.e. send emails, call APIs, push to CRM)

Works with or without human intervention

Often has memory, tools, and structured reasoning

2. Types of Ops AI Agents 🤖

Agent Type	What it Does	Example Tools
Data Fetcher + Summarizer	Pulls CRM or sheet data and writes reports	Zapier, Relevance, ChatGPT
Sales/CS Coach	Flags stale deals, gives tips	Zapier, GPT, HubSpot
Content Generator	Writes copy in your voice	Claude, ChatGPT, Jasper
Monitoring Agent	Watches CRMs, forms, or inboxes	Clay, Zapier
Routing Agent	Sorts leads based on fit or intent	Clay, Clearbit + GPT, Default

3. Best Use Cases to Start With (Marketing + RevOps) 🏗️

Each of the use cases below includes a sample prompt to help you get started fast with tools like ChatGPT, Claude, or Zapier AI Agents.

🧼 List Import Review, Cleanse & Enrichment Agent

Review new lead lists for missing fields or junk

Normalize and standardize titles and company names

Enrich with firmographic/intent data

Push cleaned list to CRM or Slack

Starting Prompt:

"Every time a new CSV list is uploaded, run the following steps:

Validate the presence of key fields: email, job title, company name.

Remove records with missing emails, personal domains, or test data.

Standardize casing and format for names and titles.

Enrich each contact with company size, industry, and tech stack.

Flag contacts with incomplete or suspicious data.

Output a cleaned CSV ready for CRM, plus a summary report."

Example Output:

Cleaned list with corrected job titles (i.e. "vp mktg" → "VP, Marketing"), enriched company size and industry, flagged 3 records with missing domains, and generated a summary message: "27 records processed, 3 flagged, 24 ready for CRM import."

🔄 Daily Pipeline Review Bot

Review open deals daily

Flag missing next steps or stale close dates

Suggest coaching points for AEs

Starting Prompt:

"Every weekday at 9:00 AM PT, connect to my HubSpot account :action: and do the following:

Retrieve all open deals from the Sales pipeline. Get owners and mark email in owner field in your report. :action:

Include only these deal stages: appointmentscheduled, qualifiedtobuy, presentationscheduled, decisionmakerboughtin, contractsent

Exclude closedwon and closedlost

Retrieve fields: dealname, dealstage, amount, closedate, createdate, hs_lastmodifieddate, hs_last_contacted, hs_next_activity_date, hubspot_owner_id

Group deals by dealstage and owner.

For each deal, identify and flag issues:

No next activity date set

Last contacted over 7 days ago

Close date has passed

Deal has not been modified in 14+ days

Score deal health using the above signals.

Mark as Low / Medium / High risk

Include a brief justification for the risk score

Generate rep-level coaching insights:

Identify patterns like frequent close date pushes or stalled stages

Suggest cleanup actions or follow-ups specific to each rep

Highlight pipeline changes vs. yesterday:

New deals added

Deals closed (won/lost)

Stage advancements or regressions

Total pipeline value delta

Highlight top 5 deals by amount, with stage, close date, and owner

For each flagged or risky deal, suggest 1–2 next steps, such as:

Follow up with contact

Adjust close date

Advance or recycle stage

Assign internal task

Generate a brief follow-up Slack message draft for high-risk deals

Format the full report as clear, actionable text or markdown:

Use sections and bullets

Include summary stats at top (total open deals, value, risk breakdown)

Keep tone direct and professional — for a RevOps + Sales audience

Send message to :action:

Do not include deals with missing names or without assigned owners.

Always check for a future meeting or next activity scheduled for each deal before assigning a negative or urgent risk score. If a future next activity is set, do not flag the deal as high or urgent risk for inactivity or lack of follow-up.

Triple-check all deals for upcoming meetings or next activities before assigning a negative or urgent risk score. Always verify for a future meeting or next activity for every deal before risk scoring.

For all deal searches, always leave the 'dealname' field blank to match any deal with a name. Never prompt for or require a specific deal name or value in deal searches.

When a search for all owners is not possible, proceed by searching for each owner individually to ensure all relevant deals are included in the workflow."

Example Output:

Slack summary: "5 open deals lack a next activity. Deal #1023 has a close date 15 days away with no contact since July 10. Recommend setting next meeting or updating stage. Coaching tip: Emphasize urgency during check-ins." Your output should be a Slack message listing flagged deals with next step issues and suggested coaching tips, e.g. "Deal #1023 is 15 days from close with no contact logged in 10 days. Recommend AE schedules follow-up."

Review open deals daily

Flag missing next steps or stale close dates

Suggest coaching points for AEs

🧠 Lead Scoring + ICP Fit Checker

Check new leads against your ICP

Enrich, score, and route automatically

Starting Prompt:

"Each time a new lead enters our system, retrieve the following fields: email, job title, company, industry, employee count, and known technology stack using enrichment tools. Do the following:

Match each lead against the following ICP criteria:

Title includes Director or higher

Works at a B2B SaaS company

Company size: 50+ employees

Uses HubSpot or Salesforce

Series B funding or later

Score each lead on a 0–10 scale based on ICP fit.

Include a short justification for the score.

Output a list of scored leads with rationale for each.

Do not score leads without a verified email domain or missing company name."

Example Output:

Scored Lead: 8/10 — Director of Marketing at Series C B2B SaaS using HubSpot. Missing intent data but meets ICP. Recommended routing to SDR with follow-up sequence A." Your output should be a score from 0–10 with a rationale for each lead, e.g. "Score: 8/10 — Director at Series C SaaS using Salesforce. ICP matched but missing firmographic data."

Check new leads against your ICP

Enrich, score, and route automatically

📈 Campaign Performance Watchdog

Monitor campaign metrics (MQLs, CPLs)

Detect anomalies and explain them

Starting Prompt:

"Every weekday at 7:00 AM PT, connect to campaign performance data in Looker Studio and Google Sheets. Retrieve MQLs, CPLs, CTRs, and form conversion rates for all active campaigns. Do the following:

Compare current data with 7-day and 30-day averages.

Detect anomalies like:

20%+ change in CPL or MQL volume

Drop in form conversions below 1%

Spike in ad spend with no return

Output plain-English summaries explaining potential root causes.

Highlight any campaigns at risk or worth pausing.

Format results as a markdown summary with campaign name, metric deltas, and commentary."

Example Output:

"Alert: Campaign 'Q3 Product Launch' had a 45% drop in MQLs and 3x increase in CPL vs. prior 7-day average. Likely cause: budget cap hit or UTM tracking failure on main form. Review ad spend and form conversion."" Your output should be a daily summary message noting performance trends, e.g. "CPL for Campaign A increased 3x. Likely cause: broken form UTM."

Monitor campaign metrics (MQLs, CPLs)

Detect anomalies and explain them

📬 Newsletter Drafting Bot

Draft content from recent posts/events

Write in your tone of voice

Starting Prompt:

"Each Monday at 8:00 AM PT, pull my 3 most recent LinkedIn posts and my most recent blog article from Notion. Use these to draft a Beehiiv newsletter in my tone (strategic, helpful, slightly sassy). Do the following:

Write an attention-grabbing subject line and opening paragraph.

Summarize key insights or lessons from my recent content.

Connect the dots in a narrative thread.

Include 1–2 CTAs (e.g. book a strategy call, read more, reply with a challenge).

Output in markdown with headline, body, and CTAs clearly separated."

Example Output:

Subject: "3 Signals You're Ready to Automate Your Ops"

Body: "Based on this week's posts and convo threads, here’s what I’ve been thinking… [summary continues]. CTA: Book a 30-min strategy chat to make this real in your org."

Tone: Strategic, witty, tactical." Your output should be a draft newsletter headline, intro paragraph, and 1–2 linked CTAs. Match my tone (strategic + sassy). Include summaries of recent content with transitions.

Draft content from recent posts/events

Write in your tone of voice

💼 Job Change Trigger Bot

Detect job changes via LinkedIn or Clay

Alert your team + suggest outreach

Starting Prompt:

"Every weekday at 10:00 AM PT, monitor job titles for contacts at our top 500 target accounts using Clay. Do the following:

Identify job changes where a contact:

Moves to a new company

Is promoted to Director or higher

Enrich new role and company info using LinkedIn and Clearbit.

Match the contact to their prior AE or CSM in our system.

Generate a Slack-ready alert message with:

Name, new title, and company

Suggested personalized outreach angle

Skip job changes that are less than 2 weeks old."

Example Output:

Slack alert: "🚨 Job Change: Jamie Chen is now VP of RevOps at Acme Corp (from Dir. Ops at Beta.io). Suggest reconnect with 'welcome back' message and book intro to new GTM team."" Your output should be a Slack message like: "🚨 Jamie Chen is now VP RevOps at Acme Corp. Previously at Beta.io. Suggest AE sends welcome-back message with renewal offer."

Detect job changes via LinkedIn or Clay

Alert your team + suggest outreach

4. Anatomy of an AI Agent (with MCP) 💀

A Model Context Protocol (MCP) is a structured specification that governs how an AI agent interacts with the world by defining its context window, memory, actions, tools, and goals. It outlines what the agent knows, what it can do, how it processes information, and how it is expected to behave in a given environment.

🔍 What's Inside an MCP (Model Context Protocol)?

Component	Purpose	Example
Persona	Defines the agent's role and tone	"You are a RevOps analyst"
Goal	What the agent is trying to achieve	"Review and clean a lead list"
Inputs	What data it receives and in what format	CSV, JSON, plain text
Memory Scope	What it remembers	Prior lists, known spam domains
Tools	What external systems it can use	Slack, HubSpot API, Clay
Output Format	Structure of its response	JSON, markdown, plain English summary
Guardrails	Limits and constraints	"Never push to CRM without validation"

🎡 Example MCP for List QA Agent

json

{

"persona": "You are a marketing analyst who validates lead lists",

"goal": "Review and enrich uploaded CSVs, flag junk, and output cleaned data",

"input_format": "CSV rows with contact data",

"memory_scope": "short_term",

"tools": ["Clearbit API", "Clay", "Slack webhook"],

"output_format": "Cleaned CSV + summary report",

"safety_checks": "Don’t send contacts with personal email domains"

}

🚧 Why MCPs Matter

Prevent hallucinations: By explicitly defining what an agent knows, what it's allowed to do, and how it's supposed to respond, MCPs dramatically reduce the risk of LLMs making things up or taking invalid actions. Guardrails in the MCP limit off-task behavior.

Standardize agent behavior: When you're scaling AI agents across teams or use cases, you want consistency. MCPs act as a template for expected input, output, and tone — so agents behave predictably and produce aligned results regardless of who triggered them.

Enable multi-step logic: MCPs allow agents to reason over multiple steps while keeping track of the task context. This is essential for workflows that involve validation, enrichment, conditional logic, and decision-making — tasks humans typically handle with experience.

Allow auditing/debugging: Because MCPs define a structured protocol, you can trace an agent’s decisions and actions after the fact. This makes troubleshooting easier and helps teams identify logic gaps, API failures, or unclear prompt design.

5. Limitations of AI Agents 🙅‍♀️

AI agents are powerful, but they’re not magic. It’s critical to understand their current limitations so you can design around them and avoid unintended consequences.

Lack of judgment: Agents can follow logic, but they don’t have human intuition. They may miss nuance or make decisions that are technically correct but contextually wrong.

Brittle without structure: Without clear prompts, protocols, or validation, agents can easily hallucinate outputs, misinterpret tasks, or break workflows.

Limited real-time context: Agents often work in snapshots — they’re only as good as the data they’re given in the moment, unless you explicitly give them long-term memory or persistence.

Tool and API fragility: If an integration breaks (i.e. HubSpot API returns an error), the agent may silently fail or produce incomplete results unless you’ve built in error handling.

Security and governance: Agents can trigger actions in sensitive systems. Without guardrails, version control, or review workflows, you risk exposing data or taking unauthorized actions.

Not autonomous (yet): Most agents today are semi-autonomous. They still need human setup, prompt tuning, and feedback. Fully autonomous, self-improving agents are still early-stage and experimental.

Understanding these limitations helps you avoid over-promising what an agent can do — and design systems that are safe, effective, and auditable.

6. Tools to Build AI Agents 🤹‍♀️

Tool	Best For	Notes / Link
Zapier AI Agents	Easy trigger/action automation	https://zapier.com/agents
Clay	Enrichment, routing, deduplication	clay.com
ChatGPT (GPT-4o)	Generalist AI with memory + tools	chat.openai.com
Claude 3.5	Structured reasoning, content drafting	claude.ai
LangChain	Developer chains and LLM workflows	langchain.com

🙂‍↔️ Pro Tip: Use ChatGPT or Claude to Help Draft Your Agent Prompts & MCPs

Use conversational AI to help structure your MCP or agent prompts:

Example prompt to ChatGPT:

"Help me write a prompt and MCP for an agent that enriches a lead list and flags bad records."

They can help you:

Write the system role and goals

Define inputs, outputs, tools, and memory

Format prompts in JSON or markdown

Tools to use:

ChatGPT with GPT-4o

Claude 3.5

Related Guides

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🧼 Data Enrichment, Cleansing, Normalization, and Validation Use Cases

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