Programmatic GTM

Build and manage your GTM agents programmatically using Claude Code or any MCP-compatible AI client connected to Relevance AI.

Programmatic GTM is the new way to build your agents for GTM in Relevance AI. Instead of clicking through a UI, you build, test, and iterate on agents, tools, and workforces directly from your coding environment — using natural language.

Setting up Programmatic GTM

Connect your AI coding environment to Relevance AI to start building programmatically.

OpenAI Codex

Connect Codex to Relevance AI with the MCP server and agent skills.

Claude Code Plugin

The fastest way to get started. Install the Relevance AI plugin for Claude Code and build agents from your terminal.

MCP Server

Connect from any MCP-compatible client — Claude Desktop, Cursor, VS Code, ChatGPT, and more.

Agent Skills

Clone the agent skills repository to give your AI coding assistant built-in knowledge of Relevance AI.

Slide Builder

Create presentations, manage BrandKits and Templates, and export slides via MCP.

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What you can do

Once connected, your AI client gets full access to your Relevance AI project. This goes far beyond running existing tools — you can build and manage your entire GTM infrastructure from clients like Claude Code.

Create agents

Design and configure new agents, set their instructions, assign tools, and configure triggers.

Build tools

Create new tools with custom steps, inputs, and outputs.

Set up workforces

Build multi-agent workflows with triggers, conditions, and agent-to-agent handoffs.

Trigger agents

Start conversations with your agents and get responses.

Execute tools

Run any of your Relevance AI tools directly from your AI client.

Troubleshoot agents

Diagnose issues with your agents by reviewing conversation logs and tool outputs.

Refine agents

Iterate on agent instructions, tool configurations, and behaviour based on real results.

Evaluate runs

Review previous agent runs, identify failures, and improve performance over time.

Update configurations

Modify agent instructions, tool settings, and workflow logic.

Create presentations

Build AI-generated slide decks, manage BrandKits and Templates, and export in multiple formats — all from your AI client.

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Use cases

Build agents

Use Programmatic GTM to create and configure agents end-to-end from your AI client. Describe what you want in natural language and let your AI client handle the setup.

Example prompts:

Customer support agent

“Create a new agent called ‘Customer Support Bot’ that answers questions using our FAQ knowledge base. Give it a friendly tone and make sure it escalates to a human when it can’t answer.”

BDR agent

“Build me a BDR agent that qualifies inbound leads from HubSpot. It should check the company size and industry, then send a personalised follow-up email via Gmail.”

Slack triage agent

“Set up an agent that monitors our Slack support channel, categorises messages by urgency, and assigns them to the right team member.”

Scheduled reporting agent

“Create an agent with a scheduled trigger that runs every morning, pulls yesterday’s sales data from Google Sheets, and posts a summary to Slack.”

Evaluate & improve

Review how your agents have been performing by looking at previous conversation runs, identifying where they succeeded or failed, and making targeted improvements.

Example prompts:

Identify failures

“Pull the last 20 conversations for my Support Agent. Identify any where the agent gave an incorrect answer or failed to resolve the issue.”

Measure qualification rates

“Look at my BDR Agent’s recent runs. How often is it successfully qualifying leads vs. letting unqualified ones through?”

Find common struggles

“Review the last week of conversations for my Onboarding Agent. Are there any common questions it struggles with? Suggest improvements to its instructions.”

Compare before & after

“Compare the performance of my Sales Agent before and after I updated its prompt last Tuesday. Is it doing better at objection handling?”

Build tools

Create custom tools that your agents can use, combining API calls, code steps, LLM processing, and integrations — all from your AI client.

Example prompts:

Company research tool

“Create a tool that takes a company URL, scrapes the homepage, and returns a one-paragraph summary of what the company does.”

Knowledge search tool

“Build a tool that searches our knowledge base for the top 3 most relevant articles given a customer question, and formats them as a numbered list with links.”

Lead enrichment tool

“Make a tool that takes a CSV of leads, enriches each one with LinkedIn data, and outputs a Google Sheet with the results.”

Cold email generator

“Create a tool that generates a personalised cold email based on a prospect’s LinkedIn profile and our product’s value props.”

Slide Builder

Create presentations, manage BrandKits and Templates, and export slides — all from your AI client without opening the Chat interface.

Example prompts:

Create a pitch deck

“Create a 10-slide investor pitch deck for our Series A round. Include slides on the problem, solution, market size, business model, traction, team, and ask.”

Build branded presentations

“Create a BrandKit called ‘Acme Corp’ using our primary colour #2563EB and Inter as the body font. Then use it to build a 6-slide product overview deck.”

Reuse a template

“Use the ‘Quarterly Business Review’ template to create a presentation for Q1 2026. Pull the metrics from this spreadsheet.”

Export slides

“Export the current presentation as a PPTX file and also as individual PNG images.”

Slide Builder via MCP

See the full list of capabilities and example prompts for Slide Builder via MCP.

Troubleshoot

When something isn’t working right, use Programmatic GTM to dig into agent behaviour, tool failures, and configuration problems.

Example prompts:

Debug triggers

“My Support Agent stopped responding to Slack messages yesterday. Check its trigger configuration and recent conversation logs to figure out what happened.”

Fix tool errors

“The lead enrichment tool is returning empty results. Look at the tool steps and check if the API call is configured correctly.”

Fix hallucination

“My agent keeps hallucinating answers instead of using the knowledge base. Review its instructions and knowledge configuration and suggest fixes.”

Audit configurations

“List all my agents and their triggers. I think one of them has a broken webhook — find it and show me the configuration.”

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Best practices

1. Plan before you build

   Before asking your AI client to create or modify anything, start by having it plan the work first. In Claude Code, you can type /plan to enter plan mode — this lets you and Claude align on the approach before any changes are made.

   Instead of jumping straight to “Build me a support agent”, start with “Let’s plan a support agent that handles inbound Slack messages. What tools will it need? What should the escalation flow look like?” — then review the plan and tell Claude to execute it.
2. Review before you approve

   When your AI client proposes changes — like updating an agent’s instructions or modifying a tool — read through what it’s about to do before confirming. This is especially important for agents that are already live and handling real conversations.

3. Use conversation history for context

   When troubleshooting or refining an agent, ask your AI client to pull recent conversation logs first. This gives it real context to work with rather than guessing.

   Prompts like “Look at the last 10 conversations and tell me what’s going wrong” are far more effective than “My agent isn’t working well, fix it”.
4. Test with real scenarios

   After building or updating an agent, trigger a test conversation to see how it actually behaves. Don’t just review the configuration — run it. Ask your AI client to “Send a test message to my Support Agent asking about refund policies” and review the response.

5. Work across multiple projects deliberately

   If you have separate projects for development and production, connect to both via separate MCP entries. Build and test in your dev project, then once you’re happy, recreate or promote the agent in production. This keeps your live agents safe while you experiment.

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Frequently asked questions (FAQs)

What is Programmatic GTM?

Programmatic GTM lets you build, manage, and iterate on your Relevance AI agents and tools directly from AI-powered coding environments like Claude Code, Cursor, or VS Code — instead of using the Relevance AI web interface. You describe what you want in natural language and your AI client handles the rest.

What is MCP?

The Model Context Protocol (MCP) is an open standard that allows AI clients to connect to external tools and data sources. It provides a standardized way for AI assistants to access your Relevance AI workspace. Programmatic GTM is built on top of MCP.

Do I need to use Claude Code?

No. Claude Code with the Relevance AI plugin provides the richest experience, but you can use any MCP-compatible client — Claude Desktop, ChatGPT, Cursor, VS Code, Windsurf, and more. See the MCP Server page for all supported clients.

Is Programmatic GTM free to use?

The MCP server and Claude Code plugin are free. You will be billed for any Relevance AI usage (agent runs, tool executions, etc.) according to your plan.

Can I use multiple AI clients at the same time?

Yes. You can connect to the Relevance AI MCP server from as many clients as you like simultaneously. Each client authenticates independently.

Does authentication expire?

Authentication tokens may expire after a period of inactivity. If you are prompted to re-authenticate, simply follow the login flow again.

Can I restrict which tools are available?

The MCP server exposes the tools and agents available in the project you authenticated against. To control access, organize your tools across different projects and authenticate each connection to the appropriate project.