Tool calling is the mechanism that transforms a language model from a text generator into an agent. When an LLM can invoke functions, query databases, and interact with APIs, it gains the ability to act on the world rather than just describe it.
These posts break down how tool-calling loops work technically, how to design tool interfaces that models use effectively, sandboxing and security considerations, and the failure modes that emerge when agents start executing real actions in production environments.
Topics include function schema design, parameter validation strategies, tool result handling and error propagation, parallel versus sequential tool execution, sandbox architectures, and the observability patterns needed to debug tool-calling chains in production. If you’re building or extending agent tool interfaces, these posts cover the implementation detail that separates working demos from reliable production systems.
Past a 10-step trajectory with read-heavy tools, your agent is bottlenecked on tool latency, not LLM throughput. Speculative tool execution fires the predicted next call while the model is still emitting tokens, then promotes or discards on commit. PASTE (arxiv 2603.18897, Microsoft Research, March 2026) reports a 48.5% task-completion-time reduction. The companion UMD/LLNL paper (arxiv 2512.15834) layers client-side and engine-side speculation for an additional 6 to 21%. The technique reduces to two design decisions: a predictor and an eligibility policy. Get either wrong and you ship a billing incident or a data-safety incident.
Most explanations of AI agents stop at “the LLM decides which tool to use.” That’s the easy part. The hard part is everything around it: how you define tools so the model actually picks the right one, how you handle failures mid-chain, and how you keep a stateless model acting like it has memory.
This post breaks down the core loop that powers every agent we build at Replyant.
The Agent Loop
Every AI agent, regardless of framework, runs the same fundamental cycle: