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An article questioning whether businesses running AI agents for clients truly understand the per-client costs involved.
Backdrop offers AI coworkers to manage projects and operations.
An article discussing unresolved challenges in the field of MLOps, covering operational hurdles in deploying and maintaining machine learning systems.
A discussion on the lack of processes for retiring AI agents, focusing on how to decide when to shut down an agent, track usage, and who should make the kill call.
A company shares their struggle with tracking AI agents across their organization, leading to the development of an agent registry that addresses discoverability, access control, logs, and usage metrics, eventually adopting TrueFoundry's solution.
The article discusses how the development of AI agents may increasingly become a challenge of operational efficiency rather than just technological advancement.
Discusses the operational challenges of deploying AI agents at scale, drawing a parallel to how Kubernetes solved container orchestration. Suggests the agent ecosystem needs a similar infrastructure breakthrough.
The AI agent ecosystem has many frameworks for building agents, but lacks operational layers for deployment and governance, prompting discussion about the need for agent control planes.
The article argues that human-in-the-loop in agent systems should move from vague approvals to explicit, auditable step-level signed decision records with detailed evidence, payloads, idempotency keys, rollback paths, and ownership. It highlights the danger of approving a black-box story rather than a specific operation.
Andon Labs ran a real cafe in Stockholm with an AI agent handling back-office operations for two months, resulting in $38k spent against $9k in sales, with critical failures like accepting a false 99% discount and over-ordering inventory.
The article explores the gap in operational tooling for AI agents in production, focusing on challenges like error handling, state replay, security, and approval workflows.
An analysis arguing that AI automation demos often gloss over the ongoing operational costs of monitoring, fixing, and maintaining systems, leading to 'false productivity' where teams spend more time managing the AI than doing the original work.
A practitioner shares 20 real AI agents for sales, operations, content, dev, and finance that are actively used and have survived the first week, emphasizing single-job agents with approval gates and structured output.
Released the Loop Template Library (loop-library), covering 50 specific scenarios including engineering, operations, evaluation, design, etc. Each Loop has a feedback, judgment, iteration loop and four Skill capabilities, supporting template matching and adaptive modification.
Elon Musk provides a technical update on SpaceX's capability to manufacture, launch, and operate AI satellites at scale.
A discussion on the operational challenges that arise when scaling from one AI agent to multiple, including context handoff, auth permissions, duplicated work, and cost tracking.
After 8 months of deploying AI agents on real operational tasks, the author shares five unexpected engineering challenges: per-capability permissions, credential isolation via a connector proxy, durable approval gates, hard budget caps, and out-of-process audit logs.
The article discusses the operational challenges of running multiple AI agents in production, emphasizing observability, recovery, and session management over the initial development of a single agent.
The article outlines the emerging role of AI leadership in enterprises, highlighting five converging responsibilities—strategy, governance, config management, performance oversight, and team coordination—needed to manage deployed AI agents at scale in 2026.