LLM Observability Tools for Reliable AI Applications
Large language models (LLMs) now power everything from customer service bots to autonomous coding agents.
Implementing Prompt Compression to Reduce Agentic Loop Costs
Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.
Implementing Permission-Gated Tool Calling in Python Agents
AI agents have evolved beyond passive chatbots.
Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel …
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The Roadmap to Mastering Tool Calling in AI Agents
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Implementing Statistical Guardrails for Non-Deterministic Agents
Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs.
Agentic RAG Explained in 3 Levels of Difficulty
Traditional
Effective KV Compression with TurboQuant
TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.
Building AI Agents in Python with Pydantic AI
Effective Context Engineering for AI Agents: A Developer’s Guide
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