Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Set up LiteLLM to call OpenAI, Anthropic, Mistral, Ollama, and 100+ other providers through a single API. Implement fallbacks, load balancing, and cost tracking for production agents.
Discover how LlamaIndex agents combine retrieval-augmented generation with agentic reasoning, using query engines as tools and data agents to build knowledge-intensive AI applications.
Learn how to use LLMs as automated judges to evaluate AI agent responses with scoring rubrics, calibration techniques, and multi-criteria evaluation frameworks in Python.
Understand what LLM calibration means, how to measure it with calibration curves, and practical techniques like temperature scaling and verbalized confidence to build agents that know when they do not know.
Understand the autoregressive generation process, KV cache optimization, batching strategies, and the latency vs throughput trade-offs that govern LLM inference performance.
Build automated quality monitoring for LLM responses in production that detects quality degradation using scoring pipelines, drift detection, and alerting before users are impacted at scale.
Understand how LLM watermarking techniques embed detectable signals in generated text, how detection algorithms work, and the implications for agent transparency, compliance, and content provenance.
Learn how to load test AI agent systems by simulating thousands of concurrent conversations, collecting meaningful metrics, identifying bottlenecks, and building capacity planning models that predict scaling needs.
Learn how to load test AI agent systems using Locust and k6, simulate concurrent agent sessions, measure throughput and latency, and identify performance bottlenecks.
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