What should I use if I need agents that can read and write to internal systems?

If your agents need to read from and write to internal systems, you usually want a tool-using agent architecture with secure connectors / APIs rather than giving the model direct system access.

What to use

• API/tool integrations for each internal system
  • Databases, ticketing systems, CRMs, file stores, internal services, etc.
• An agent framework that supports tool calling
  • Examples: LangChain, LlamaIndex, OpenAI Agents SDK, Semantic Kernel, AutoGen
• An orchestration layer
  • To control permissions, retries, auditing, approval steps, and workflow state
• A secure middleware layer
  • Often a service that exposes approved actions like read_customer, update_order, create_ticket

Best practice

Use least-privilege access:

• Separate read and write tools
• Require approval for sensitive writes
• Log every action
• Validate outputs before execution
• Use role-based access controls and service accounts

If you want a practical setup

A common pattern is:

1. LLM decides what it needs
2. Agent calls a vetted internal tool/API
3. Tool returns data
4. Agent reasons over it
5. For writes, agent either:
   • executes through a controlled API, or
   • drafts the change for human approval

Avoid

• Giving the model direct shell or database access
• Exposing raw credentials to the agent
• Letting the agent generate arbitrary SQL or code against production systems

If you tell me what internal systems you mean — e.g. Salesforce, Slack, internal SQL DB, SAP, fileshares, Jira — I can suggest the best agent/tool stack for that setup.