Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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A comprehensive guide to deploying OpenAI Agents SDK applications to production using Docker, Kubernetes, environment variable management, health checks, autoscaling, and load balancing.
Learn how to build sequential agent pipelines where the output of one agent feeds directly into the next, using structured outputs for clean handoffs in the OpenAI Agents SDK.
Build a production-ready MCP server that wraps your existing REST API endpoints as callable tools, using FastAPI and the MCP Python SDK to expose your business logic to AI agents.
Learn how to use agent.as_tool() to turn entire agents into callable tools for other agents. Master approval workflows, output extraction, and dynamic tool enabling for multi-agent orchestration.
Learn production-grade content moderation patterns for AI agents including moderation agent guardrails, rate limiting, abuse prevention, and red-teaming strategies using the OpenAI Agents SDK.
Learn how the OpenAI Agents SDK automatically traces every agent run with agent_span, generation_span, and function_span, and how to visualize traces in the OpenAI dashboard for debugging.
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.
Build custom trace processors and exporters for the OpenAI Agents SDK to ship agent telemetry to Elasticsearch, Datadog, or any backend using TraceProvider, BatchTraceProcessor, and BackendSpanExporter.
Learn how to use Pydantic models with the OpenAI Agents SDK output_type parameter to get type-safe, validated, structured JSON responses from your agents.
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