Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Integrate Anthropic, Google, Mistral, and other LLM providers into OpenAI's Agents SDK using LiteLLM's unified interface with LitellmModel, provider prefix notation, and cross-provider tracing.
Use MCP prompt resources to dynamically load and parameterize agent instructions from external servers, enabling centralized prompt management with list_prompts and get_prompt.
Secure your MCP-powered agents for production with authentication, network policies, tool approval workflows, audit logging, rate limiting, and defense-in-depth strategies.
Use MCPServerManager to orchestrate multiple MCP server connections with automatic failure detection, reconnection strategies, and health monitoring using active_servers, failed_servers, and drop_failed_servers.
Master MCPServerStdio for connecting agents to local tool servers via standard I/O, including subprocess management, npx-based servers, filesystem operations, and automatic lifecycle handling.
Connect agents to remote MCP tool servers using MCPServerStreamableHTTP with authentication headers, timeout configuration, retry policies, tool caching, and production deployment patterns.
Understand MCP, the open protocol for connecting AI agents to external tools and data sources, including its architecture, five transport types, and how to build your first MCP-connected agent.
Learn how to choose the right OpenAI model for each agent in your system, comparing GPT-4.1, GPT-5, and GPT-5-mini across cost, latency, reasoning capability, and tool-use accuracy.
Learn the two core multi-agent orchestration patterns in the OpenAI Agents SDK — Agents as Tools (Manager) and Handoffs (Delegation) — with code examples and a decision framework for choosing the right approach.
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