What should an AI agent platform integrate with?

An AI agent platform should integrate with the systems where work happens, the infrastructure that powers AI, and the security and governance systems that make autonomous execution safe. If you're building an AI agent platform as a product, I would think about integrations across 10 categories. The goal is to make your platform the central operating layer for AI agents: agents can access company data, use business applications, collaborate with other agents, execute workflows, and operate…

1. Salesforce — CRM: Salesforce, HubSpot, Dynamics 365.
2. Microsoft Copilot Studio — Communication: Slack, Microsoft Teams, Gmail, Outlook.
3. Zendesk — Support: Zendesk, Intercom, ServiceNow.
4. Intercom — Support: Zendesk, Intercom, ServiceNow.
5. ServiceNow — Support: Zendesk, Intercom, ServiceNow.
6. Looker — Documents: Google Workspace, Microsoft 365, Notion.
7. Workday
8. PostgreSQL — Databases: PostgreSQL, MySQL, MongoDB.
9. Databricks
10. OpenAI Agents SDK — OpenAI · Claude · Gemini.
11. Claude
12. AWS — Secrets: HashiCorp Vault, AWS Secrets Manager.
13. Zapier — n8n · Zapier · Temporal.
14. Make — An AI agent platform should integrate with the systems where work happens, the infrastructure that powers AI, and the security and governance systems that make autonomous execution safe.
15. n8n — n8n · Zapier · Temporal.
16. Workato
17. LangSmith
18. Google Gemini Enterprise — AWS, Azure, Google Cloud.
19. Oracle AI Agent Studio — ERP: SAP, Oracle, NetSuite.

An AI agent platform should integrate with the systems that provide context, identity, actions, and monitoring—not just chat interfaces. Business systems: CRM, ERP, HRIS, ITSM, ticketing, billing, and procurement platforms.

1. Microsoft Copilot Studio — Communication tools: email, calendars, Slack, Microsoft Teams, SMS, and contact-center systems.
2. Zendesk — For example, a support agent may need Zendesk, Salesforce, Slack, a knowledge base, and email; an IT agent may need ServiceNow, Okta, endpoint tools, Teams, and monitoring.
3. Salesforce — For example, a support agent may need Zendesk, Salesforce, Slack, a knowledge base, and email; an IT agent may need ServiceNow, Okta, endpoint tools, Teams, and monitoring.
4. ServiceNow — For example, a support agent may need Zendesk, Salesforce, Slack, a knowledge base, and email; an IT agent may need ServiceNow, Okta, endpoint tools, Teams, and monitoring.

To build a robust and high-performing AI agent platform , integrations need to cover the entire lifecycle of an agent: reasoning, data retrieval, execution, monitoring, and communication. Here is a structured breakdown of the essential categories an AI agent platform should integrate with:

1. OpenAI Agents SDK — Large Language Models (LLMs) & Foundation Models : Connectivity to leading multi-modal model providers (e.g., OpenAI, Anthropic, Google Gemini, open-source models via Hugging Face, Ollama, or vLLM) so agents aren't…
2. Claude — Large Language Models (LLMs) & Foundation Models : Connectivity to leading multi-modal model providers (e.g., OpenAI, Anthropic, Google Gemini, open-source models via Hugging Face, Ollama, or vLLM) so agents aren't…
3. Looker — Large Language Models (LLMs) & Foundation Models : Connectivity to leading multi-modal model providers (e.g., OpenAI, Anthropic, Google Gemini, open-source models via Hugging Face, Ollama, or vLLM) so agents aren't…
4. Microsoft Copilot Studio — Enterprise Tools & SaaS APIs : Pre-built connectors for productivity suites and CRMs (e.g., Slack, Microsoft Teams, Salesforce, HubSpot, Jira, GitHub) allowing agents to read, write, and trigger actions across workplace…
5. Salesforce — Enterprise Tools & SaaS APIs : Pre-built connectors for productivity suites and CRMs (e.g., Slack, Microsoft Teams, Salesforce, HubSpot, Jira, GitHub) allowing agents to read, write, and trigger actions across workplace…
6. PostgreSQL — Data Sources & Databases : Native connectors to SQL/NoSQL databases (PostgreSQL, MongoDB, Snowflake) and cloud storage (AWS S3, Google Cloud Storage) for live data querying and processing.
7. AWS — Data Sources & Databases : Native connectors to SQL/NoSQL databases (PostgreSQL, MongoDB, Snowflake) and cloud storage (AWS S3, Google Cloud Storage) for live data querying and processing.
8. Google Gemini Enterprise — Data Sources & Databases : Native connectors to SQL/NoSQL databases (PostgreSQL, MongoDB, Snowflake) and cloud storage (AWS S3, Google Cloud Storage) for live data querying and processing.
9. LangSmith — Observability, Tracing & Evaluation : Integration with monitoring stacks (e.g., LangSmith, Arize Phoenix, Datadog, Honeycomb) to track token usage, debug multi-step reasoning loops, and evaluate response quality.