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TL;DR Tool calling enables AI agents to invoke external capabilities APIs, databases, search, and workflows during inference. Efficient execution depends on deterministic planning, low-latency routing, robust observability, and evaluations that quantify correctness and cost. Engineering teams should standardize on an AI gateway with distributed tracing, semantic caching, failover, and

TL;DR Agent loops occur when an AI agent repeatedly cycles without making progress. Prevent them by combining robust tracing, loop-aware patterns, targeted alerts, deterministic safe fallbacks, and strict tool governance. Use Maxim AI’s end-to-end stacks: simulation, evaluations, observability, and Bifrost gateway, to detect, reproduce, and remediate loops across

Context window management has emerged as a critical challenge for AI engineers building production chatbots and agents. As conversations extend across multiple turns and agents process larger documents, the limitations of context windows directly impact application performance, cost, and user experience. Modern language models offer context windows ranging from 8,