Most engineers troubleshooting friction with AI coding agents have been looking in the wrong place. The instinct is to sharpen prompts, tweak system instructions, or switch models. But AWS Solutions Architects are making a different argument: the friction is architectural. Traditional cloud infrastructure was designed for human development cadences — slow

Every production agent eventually collides with the same fundamental problem: the session is stateless, but the user's context isn't. Prior conversations, domain rules, and user preferences need to survive across runs — and most teams end up duct-taping Redis and a vector database together without any principled

Most conversations about agent safety focus on capability restrictions — don't let agents delete files, don't let them send emails without approval. SEVerA (Self-Evolving Verified Agents) targets a harder and less discussed problem: when an agent autonomously rewrites its own programs, what guarantees do you have that

A new diagnostic framework called TRAJEVAL has produced one of the clearest quantifications of coding agent inefficiency yet recorded: across 16,758 agent trajectories, all agents — regardless of architecture or underlying model — examined approximately 22 times more functions than a reference patch actually required. The framework decomposes agent execution into

A new benchmark called SlopCodeBench has produced what may be the most sobering result in agentic coding research to date: not a single agent from any of 11 models tested could complete an iterative coding task end-to-end. The highest checkpoint solve rate across all models was just 17.2%. More

The gap between a working agentic prototype and a production enterprise deployment is not primarily a capability gap — it's an operational and governance gap. A prototype succeeds when the agent does the right thing most of the time. A production system needs to handle coordination failures, audit every

Teams building AI agents have a consistent bias: when in doubt, add more agents. Multi-agent architectures sound sophisticated, and the marketing around agentic AI doesn't help — but in practice, most teams that go multi-agent early end up with bloated systems, wasted token budgets, and debugging nightmares that a

MCP gave AI agents the ability to take actions — call tools, write files, execute code. The parallel infrastructure challenge, less discussed but equally important, is giving agents the ability to perceive: to read, navigate, and extract information from the open internet. A new data-backed analysis from Phase Transitions AI maps

Most engineers building AI agents think about two things: what they tell the model (prompt engineering) and what data they give it (context engineering). A third layer — harness engineering — is often missing entirely, and it's where production systems quietly break down. Louis Bouchard draws a sharp distinction between

An enterprise automation architect writing about healthcare payer systems has produced one of the most transferable engineering posts of the week — not because of the domain, but because of the design patterns. The core argument: deterministic RPA workflows don't fail because they're old, they fail because

A comprehensive new survey traces the full arc of how LLM agents have learned to use tools — from the earliest experiments asking "did the model pick the right tool?" all the way to today's multi-agent systems navigating long, branching trajectories with real-world side effects. The paper

A new benchmark from the National University of Singapore puts today's best AI code review agents to the test — and the results are sobering. c-CRAB (Code Review Agent Benchmark) is the first evaluation framework built specifically around the task of reviewing pull requests rather than writing code. By