Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Master LangGraph state management with TypedDict schemas, annotation reducers for message lists, custom state channels, and strategies for complex multi-step agent workflows.
Langgraph official docs stateful multi-agent workflows: learn LangGraph's graph-based approach to building stateful, multi-step AI workflows — including nodes, edges, conditional routing, state management, and human-in-the-loop patterns.
Implement real-time streaming in LangGraph with stream modes for node-level updates, token-by-token LLM output, custom event streams, and practical patterns for responsive agent UIs.
Learn how to integrate LLM function calling into LangGraph workflows using ToolNode, tool binding, automatic tool execution, and structured error handling for reliable agent behavior.
Set up LangSmith for tracing LangChain runs, analyzing run trees, building evaluation datasets, running automated evaluations, and collecting feedback on LLM outputs.
Build multilingual AI agents with language detection, translation API integration, quality assessment, and fallback strategies that handle real-world linguistic diversity.
A hands-on guide to measuring AI agent latency at every stage of the pipeline, from time-to-first-token through tool execution to total response time, with percentile reporting and SLA compliance tracking.
Learn how to create and enforce latency budgets for real-time AI systems, breaking down time allocation across network, preprocessing, inference, tool execution, and response delivery layers.
Learn systematic approaches to profile AI agent latency, identify bottlenecks in multi-step pipelines, and apply targeted optimizations using timing decorators, flame charts, and parallel execution patterns.
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