Most RevOps teams talking about AI agents are still just using chatbots. They paste a deal summary into ChatGPT, get a paragraph back, and call it automation. That is not an agent. A true AI agent watches your systems, makes decisions based on context, and takes action without you prompting it. The good news: building your first one is simpler than you think, and the payoff is immediate.
AI Agent vs. Chatbot: Why the Distinction Matters¶
A chatbot waits for input. You ask, it answers. An AI agent is proactive. It runs on a schedule or trigger, evaluates data against a set of goals, and executes tasks autonomously. For RevOps, that difference is everything.
| Feature | Chatbot | AI Agent |
|---|---|---|
| Trigger | User prompt | Event, schedule, or data change |
| Decision-making | Responds to questions | Evaluates context and chooses actions |
| Integration | Standalone interface | Embedded in your stack (CRM, MAP, etc.) |
| Output | Text response | Data changes, notifications, workflow triggers |
When you build an agent, you are building a digital teammate that handles a specific job without supervision.
The Best First Agent: A CRM Data Quality Checker¶
Start with a CRM data quality agent. It is low-risk, high-impact, and teaches you the core patterns you will reuse for every future agent. This agent scans your CRM on a schedule and checks for:
- Missing required fields (e.g., industry, employee count, lead source)
- Formatting inconsistencies (e.g., phone numbers, company names with “Inc” vs “Inc.”)
- Stale records (e.g., open opportunities with no activity in 30+ days)
- Suspected duplicates based on fuzzy matching of name and domain
Tools You Need¶
You do not need an enterprise AI platform. Here is a lean stack that works:
- LLM API - OpenAI GPT-4o or Anthropic Claude for decision logic
- Orchestration layer - LangChain, CrewAI, or a simple Python script with a scheduler
- CRM connector - Native API (Salesforce REST API, HubSpot API) or a tool like Workato
- Data store - A lightweight database (PostgreSQL or even Google Sheets) to log agent actions
- Notification channel - Slack or email for flagged issues
Implementation Steps¶
Step 1: Define your rules. Write out every data quality check as a clear rule. “If Industry is blank and Lifecycle Stage is MQL or later, flag it.” The more specific, the better.
Step 2: Build the extraction layer. Write a script that pulls records from your CRM via API. Start with a single object - Contacts or Companies - and limit to records modified in the last 7 days.
Step 3: Add the LLM decision layer. For straightforward rule checks, you do not even need an LLM. But for fuzzy tasks - like detecting duplicates or standardizing company names - pass the data to an LLM with a structured prompt and request JSON output.
Step 4: Define actions. Decide what the agent should do when it finds an issue. Options range from conservative (log to a spreadsheet for human review) to aggressive (auto-update the CRM field). Start conservative.
Step 5: Schedule and monitor. Run the agent daily via a cron job or cloud scheduler. Log every action so you can audit what it changed and refine its rules over time.
Pro tip: Measure your baseline data quality score before deploying the agent. Track the same score weekly. This gives you a concrete ROI number to share with leadership.
Common Mistakes to Avoid¶
- Going too broad too fast. Pick one object and five rules. Expand later.
- Skipping the logging step. If you cannot see what the agent did, you cannot trust it.
- Over-relying on the LLM. Use deterministic rules where possible and save AI for genuinely ambiguous tasks.
Key Takeaways¶
- An AI agent is not a chatbot - it acts autonomously on triggers, not prompts
- A CRM data quality checker is the ideal first agent because it is low-risk and high-impact
- You can build a working agent with a CRM API, a simple Python script, and an LLM API
- Start conservative with human-in-the-loop actions and expand autonomy as trust grows
- Always log agent actions and measure outcomes against a baseline