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AI Gateway's May 2026 data shows DeepSeek's token share surged to 17% with minimal spend, while Anthropic retained 65% of spend, indicating cost-conscious routing and growing overall usage.
Salesforce deployed 20,000 enterprise AI agents, revealing that the majority of effort comes after launch, not before. John Kucera, CPO of Agentforce, shares lessons on what separates successful agents from those that stall.
The article examines why internal enterprise AI projects often stall after the demo stage, highlighting operational challenges such as schema mapping, metric definitions, and maintaining trust, while noting that the AI model itself is the easiest part.
This article introduces the concept of 'Harness Engineering,' a discipline focused on designing the systems that constrain and guide AI agents to make them reliable in production, arguing that the harness matters more than the model itself.
A community discussion asking practitioners which AI agent orchestration framework—LangGraph, CrewAI, AutoGen, or OpenAI Agents—is most production-ready and scales well in real deployments.
Discusses how AI agents for SMB verticals often degrade after launch due to context drift — changes in business operations that the agent doesn't automatically reflect — and suggests solutions like syncing with existing business tools and limiting agent scope.
A comprehensive mid-2026 survey of the AI agent ecosystem covering 25+ frameworks, showing 57% of organizations have agents in production, alongside major funding rounds and enterprise deployments.
The article warns that when migrating to a new embedding model in production, previously calibrated trust scores and thresholds become invalid, yet the system may still produce plausible but subtly wrong outputs, causing silent degradation.
This essay argues that evaluation is the hardest problem in production AI, not generation, and decomposes AI self-knowledge into calibration, discrimination, and expression, with implications for system design.
An op-ed discussing the gap between AI code generation and production-grade systems, emphasizing that human judgment and domain expertise remain critical for orchestrating interconnected decision loops in complex domains.
This article argues that the narrative that only frontier AI models are necessary for production is driven by financing needs, not architectural reality. It highlights that smaller, efficient models like Phi-4, Claude Haiku, and routing solutions like RouteLLM offer cost-effective alternatives, and most enterprises waste tokens by defaulting to large models.
The article highlights three common failure modes in production AI memory systems: outdated preferences persisting, sarcasm stored as literal, and summaries outliving their source facts. It argues that the AI memory industry lacks provenance, confidence scores, and versioning, creating a black-box problem that hinders debugging.
A review of five agentic AI workflow builders that actually work in production, highlighting SimplAI as a standout enterprise agent operating system and discussing the importance of workflow layer over model quality.
While 72% of teams use coding agents in production, most lack formal governance or empirical data on agent reliability. The article argues for session-level tracking over policy frameworks to ensure trust in critical deployments.
A developer shares their experience of a single system prompt change degrading LLM response quality without triggering traditional monitoring alerts, and describes internal tooling they built to monitor semantic quality in production LLM applications.