Technical articles on systems architecture, agent execution, and operational patterns.
Why traditional APM tools fall short for agent workloads, and how to build observability that captures token usage, decision traces, and tool call latencies.
Exploring working memory, episodic memory, and semantic memory architectures for agents that need to maintain context across hundreds of interactions.
How to design agent execution loops that handle failures gracefully — covering retry strategies, exponential backoff, circuit breakers, and graceful degradation patterns for production AI systems.
Practical patterns for designing tool APIs that LLMs can actually use reliably — covering parameter design, error messages, and output formatting.
Battle-tested strategies for getting reliable structured data from LLMs — covering JSON mode, schema validation, retry logic, and partial extraction.
Strategies for managing the context window across long conversations — sliding windows, summarization, priority eviction, and hybrid approaches.
Why accuracy alone is a misleading metric for agent systems, and how to build evaluation frameworks that capture cost efficiency, latency, reliability, and user trust.