Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Learn to build an AI agent that analyzes source code, identifies untested paths, and generates high-quality unit and integration tests with proper assertions, fixtures, and edge case coverage.
Optimize the database layer that powers your AI agent's knowledge retrieval with query profiling, index design, materialized views, and query caching strategies that cut latency from seconds to milliseconds.
Build a complete help desk system with AI ticket classification, automatic agent assignment, SLA tracking, escalation workflows, and a reporting dashboard for support team performance.
Learn how prefix tuning and soft prompts let you customize LLM behavior by training small continuous vectors prepended to model inputs, achieving fine-tuning-level performance at a fraction of the cost.
Build a monitoring system for AI agent data pipelines that tracks ingestion metrics, detects data drift, alerts on failures, and enforces SLAs to keep your agent's knowledge base fresh and reliable.
Implement explainability in AI agents with decision logging, confidence communication, and user-facing explanation interfaces that build trust without sacrificing performance.
Learn how to rigorously benchmark vector databases with proper methodology — measuring latency, throughput, and recall under realistic conditions to make informed infrastructure decisions.
Learn how to version control your AI prompts using Git. Covers file-based prompt storage, meaningful diffs, branch strategies for prompt experiments, and rollback techniques for production safety.
Master complex agent routing with conditional handoff logic, multi-step handoff chains, runtime agent creation, and context transformation between agents in the OpenAI Agents SDK.
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