Agentic AI doesn’t replace traditional automation. It uses existing workflows, APIs, and systems as tools while handling decisions, exceptions, and complex tasks.
The latest MCP spec mandates W3C tracing. Use Quarkus and OpenTelemetry to easily visualize disjointed, multi-round-trip AI agent workflows in production.
AI coding assistants are deeply fluent in current Spring conventions, which raises a real question: does that lock architecture into 2020-era patterns? It turns out, no.
Root searches are cached and easy to access. Filtering forces requests to hit the backend database directly, triggering stricter anti-bot checks that block basic proxies.
AI deployment demos always end before the hard part begins. Writing code is 20% of delivery. This article breaks down the hidden 80% that demos never show you.
CI optimization using Git diff and JGit to selectively run impacted Karate tests, reducing regression execution while preserving safe fallback coverage.
The future of AI agents is searchable tool discovery, not hardcoded APIs. MCP and semantic search are turning tools into capabilities agents can find and use at runtime.
AI agents can take over the first minutes of incident response, but only with the right boundaries. Seven guardrails that keep an SRE agent from becoming the outage.
Building a dynamic API translation proxy that leverages Java 21 Virtual Threads and Redisson distributed locking to safely execute AI-driven schema mapping.
A standardized instruction layer for enabling AI agents to accurately navigate, build, and test applications by enforcing clear architectural and operational constraints.
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