Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Learn to accurately track and optimize the total cost of AI agent conversations including token usage, tool call expenses, infrastructure overhead, and strategies for reducing cost per interaction.
Build a complete cost tracking system for AI agents that attributes token usage to individual users and features, sets budget alerts, and provides dashboards for controlling LLM spend in production.
Master the art of designing effective CrewAI agents by crafting specific roles, meaningful backstories, aligned goals, and configuring verbose mode for transparent agent reasoning.
Implement step callbacks, task callbacks, and custom event handlers in CrewAI to monitor agent reasoning in real time, log progress, and build observable multi-agent systems.
Configure CrewAI agents to use different LLM providers including Anthropic Claude, local Ollama models, and Azure OpenAI, with model parameter tuning and fallback strategies.
Learn how CrewAI organizes agents into role-based teams with defined tasks, delegation patterns, and process flows to build collaborative AI systems that tackle complex workflows.
Learn how to install CrewAI, define agents with the Agent class, create tasks with the Task class, assemble a Crew, and run it with kickoff to build your first multi-agent workflow.
Configure CrewAI's three memory systems — short-term for session context, long-term for cross-session learning, and entity memory for tracking people and concepts — with storage backends and embedding options.
CrewAI sequential vs hierarchical vs consensual processes: what each one does, when it breaks, and how to pick the right process for your crew.
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