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Long-form notes on software, distributed systems, and the craft of building. Shipping one a week.
Reading AG-UI as a wire protocol, not a framework
I kept rebuilding the same SSE envelope every time I wrote an agent UI. AG-UI is the first serious attempt I have seen at standardising that envelope. In this post I strip the protocol down to its wire shape and rebuild a minimal Spring WebFlux endpoint that speaks it without an SDK.
Code Graphs for Coding Agents: The Delivery Shape Matters More Than the Algorithm
I spent a weekend pointing a coding agent at a 480k-line Go monorepo and watching it grep-loop through 38 tool calls on one question. AST-derived code graphs fix that, but the delivery shape — local stdio MCP, remote service, or skill — changes the economics more than the graph algorithm does. Here is where I would put one in 2026, with a minimal Go indexer I can drop next to the agent.
Exposing Spring AI Agents via the A2A Protocol: What Interoperability Actually Buys You
Spring AI's server-side A2A integration is stable enough to put in production, but the protocol is most useful at organizational boundaries, not as an internal RPC replacement. This post walks through what actually changes in a Spring AI codebase, where the sharp edges still are, and a practical decision framework for A2A vs MCP vs plain REST.
JetBrains Tracy: Pragmatic AI Observability for Kotlin
JetBrains Tracy is a Kotlin library that wires LLM-aware tracing into your app on top of OpenTelemetry. This post walks through how I integrated it in a Spring Boot service, the design decisions that matter, and the failure modes teams hit once LLM calls become the hottest path in their system.
The Deterministic Backbone: Why Production AI Systems Are Moving Away From Fully Autonomous Agents
Fully autonomous agents are hard to bound, hard to test, and expensive to operate. A deterministic backbone with narrow agent steps gives you the control flow back while keeping the intelligence where it matters. Here is how to design, test, and migrate toward it.
Memory Evaluation: Measuring How AI Memory Decays Over a Project's Lifetime
Most AI memory benchmarks grade on recall and stop there. That hides the real failure mode: stale facts quietly poisoning the context window. Here is a lifecycle-based evaluation framework that tests recall, revision, and controlled forgetting across the change points every long-lived project goes through.