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