Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Learn how to package AI agents into portable, versioned bundles with dependency management, configuration schemas, and reproducible deployments. Build a packaging format that works across teams and environments.
Define and measure Service Level Agreements for AI agent systems with practical guidance on SLA definition, measurement methodology, automated reporting, and penalty handling for production agent deployments.
Learn how to craft system instructions that produce consistent, reliable agent behavior — covering instruction engineering, behavioral boundaries, persona design, and techniques for preventing instruction drift.
Learn how AI agents break down complex goals into executable steps using task decomposition, hierarchical planning, plan-and-execute architecture, and dynamic replanning strategies.
Build a reputation system that tracks agent reliability and output quality over time. Learn scoring mechanisms, trust propagation, penalty systems, and how to rehabilitate underperforming agents.
Design effective agent response formatting using Markdown rendering, rich card components, interactive elements, tables, code blocks, and responsive layouts for chat interfaces.
A practical framework for deciding when to create specialized single-purpose agents versus general-purpose agents. Covers capability mapping, cost-quality tradeoffs, and real-world decision criteria.
Learn how to model AI agent workflows as finite state machines with explicit states, transitions, and guards — providing predictable behavior, easy debugging, and reliable persistence for long-running tasks.
Discover how swarm intelligence principles like stigmergy, ant colony optimization, and particle swarm optimization can be applied to multi-agent AI systems. Includes Python implementations of each pattern.
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