Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Implement the Chain of Responsibility pattern for AI agents with fallback chains, capability matching, and cost-optimized ordering to handle requests efficiently.
Implement the Strategy pattern to dynamically swap AI agent behaviors at runtime — supporting A/B testing, context-driven model selection, and flexible agent configuration.
Use the Builder pattern to create fluent, validated, and immutable agent configurations — replacing sprawling constructors with readable step-by-step builder classes.
Implement the Circuit Breaker pattern to protect AI agent systems from cascading failures with automatic failure detection, open/half-open/closed states, and graceful recovery.
Implement the Saga pattern for AI agent systems to manage multi-step transactions with compensating actions, rollback support, and saga orchestration for reliable distributed workflows.
Learn a practical decision framework for choosing between prompt engineering, retrieval-augmented generation, and fine-tuning based on cost, data requirements, latency, and use case complexity.
A complete walkthrough of fine-tuning models through the OpenAI API, covering data preparation in JSONL format, file upload, training job creation, evaluation, and deploying your custom model.
Master the art of building high-quality fine-tuning datasets with practical techniques for data collection, cleaning, deduplication, format validation, and diversity analysis.
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