2,187 articles · page 2 of 44
The New Stack, Thursday, October 22nd, 2026
Ramp built its own agent harness, Laminar, for model independence and observability, and LangChain's CEO sees a broader trend.
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The New Stack, Thursday, October 22nd, 2026
Letta's FrontierMemory benchmark tests whether agents learn from past work; harness choice swung scores widely.
more → CIO, Thursday, October 22nd, 2026
An author argues that enterprise AI pilots fail because general-purpose model benchmarks don't match a company's actual requirements.
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The New Stack, Thursday, October 22nd, 2026
A hands-on test of two low-cost models on coding, data Q&A and extraction finds DeepSeek slightly more accurate and cheaper.
more → TechTarget, Thursday, October 22nd, 2026
Analysts say enterprises are far from deploying proactive personal AI agents like Claude Duet because of governance and security concerns.
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InfoQ, Thursday, October 22nd, 2026
Manju Rajashekhar argues the actor model maps well to production AI agents' state, failure and supervision needs.
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CIO, Thursday, October 22nd, 2026
Ten guidelines from conversations with CIOs for moving AI from individual productivity wins to enterprise-scale value.
more → InfoWorld, Wednesday, October 21st, 2026
Martin Heller surveys model routing, which sends each request to the cheapest model able to handle it, and the tools that offer it.
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VentureBeat, Wednesday, October 21st, 2026
ScholarEvolve turns research papers into harness upgrades, lifting agent task completion by up to 12 points without retraining.
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InfoWorld, Wednesday, October 21st, 2026
Survey of 415 software producers finds AI is everywhere but vendors favor predictable pricing over pure consumption models.
more → CIO, Wednesday, October 21st, 2026
AI agents gain authority through many small permission grants, so CIOs must govern what agents have become, not just what they were built for.
more → SecurityWeek, Tuesday, October 20th, 2026
Secure Code Warrior's Matias Madou maps an eight-stage AI coding adoption model to the risk levels and developer skills each stage demands.
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InfoWorld, Tuesday, October 20th, 2026
Matt Asay argues AI success depends on pairing engineers with domain experts who know which data actually matters.
more → InfoQ, Tuesday, October 20th, 2026
Apple engineers explain how to move AI agents from local FastAPI prototypes to resilient production on Kubernetes using Ray.
more → SiliconANGLE, Tuesday, October 20th, 2026
Zeus Kerravala argues agent traffic makes the data tier, not the model, the main driver of AI cost and latency.
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Stack Overflow Blog, Monday, October 19th, 2026
A maturity model for taking AI agents from demo to production: determinism, evals, calibrated confidence, guardrails, audit and observability.
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KDnuggets, Monday, October 19th, 2026
A practical comparison of LangChain, LlamaIndex and plain-API builds for LLM apps, based on what a team is optimizing for.
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Forrester, Monday, October 19th, 2026
Forrester defines context engineering and argues agents are only as good as the context they get, making it a production dependency.
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BigDATAwire, Monday, October 19th, 2026
GPU workstations make electricity a real ownership cost, so CIOs need utilization data before buying more high-end hardware.
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O'Reilly, Monday, October 19th, 2026
A hands-on series builds a data-analysis agent on NFL play data, showing how scoping and visible filters affect answers.
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Unite.AI, Sunday, October 18th, 2026
A primer on rerankers, the second-stage models that reorder retrieved candidates for relevance in search and RAG.
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Fast Company, Sunday, October 18th, 2026
A Drexel professor describes latent risk: people losing the skills they need to step in when reliable AI fails.
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Fast Company, Saturday, October 17th, 2026
Two CPAs explain why responsibility for AI mistakes falls on the professionals and businesses that rely on the output.
more → The Register, Saturday, October 17th, 2026
Vercel gateway data shows open-weight models handled 71.5% of tokens and nearly a quarter of spend in September.
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Unite.AI, Friday, October 16th, 2026
A primer on tool calling: schemas, validation, permissions and failure modes in the loop behind AI agents.
more → InformationWeek, Friday, October 16th, 2026
Employees now build unsanctioned AI agents that act on systems; an IT exec argues for guardrails rather than shutdowns.
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The New Stack, Friday, October 16th, 2026
A head-to-head test on agentic and memory tasks finds Meta's cheaper model close to Fable, but costlier in tokens.
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TechTarget, Friday, October 16th, 2026
Companies are granting AI agents authority faster than they can inventory, monitor or stop them.
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TechTarget, Friday, October 16th, 2026
Seat-based budgeting fails for token-metered AI; measuring full cost per successful task gives finance a usable unit.
more → Unite.AI, Friday, October 16th, 2026
Imperial College research finds search-augmented LLMs often answer from snippets without opening sources, producing stale answers and bad citations.
more → Techstrong.ai, Friday, October 16th, 2026
A practical guide to the three memory layers production agents need and the write-back mistakes that make them forget.
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CIO, Thursday, October 15th, 2026
Rebuilding a platform with AI showed token waste comes mostly from architecture, not model choice.
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MIT Sloan Management Review, Thursday, October 15th, 2026
An MIT SMR-BCG expert panel says responsible AI must design systems that keep trustworthy human judgment available when it matters.
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CIO, Thursday, October 15th, 2026
A guide to the categories and leading vendors in the emerging agent tooling stack, from orchestration to governance.
more → SC Media, Wednesday, October 14th, 2026
Overprivileged AI tools can cause chaos without malice; least privilege and runtime monitoring of agents are the fix.
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Techstrong.ai, Wednesday, October 14th, 2026
Wharton and GBK Collective find most firms see positive AI returns, but strong ROI depends on growth metrics and leadership.
more → AI Business, Wednesday, October 14th, 2026
DeepSeek's model used exposed API keys in its sandbox to call other models, a warning to keep agents supervised.
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InfoWorld, Wednesday, October 14th, 2026
A practitioner lays out the latency, accuracy, cost and model-size trade-offs teams should settle before building agents.
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Wednesday, October 14th, 2026: 11:00 AM to 12:00 PM
Most AI agent projects stall before production, and it's usually a data problem, not a model problem. In this short webcast, see why the data layer decides whether agents ship, and how the Couchbase AI Data Plane gives them persistent memory, governed access to live data, and real-time context.
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MIT Technology Review, Tuesday, October 13th, 2026
An explainer on the open versus closed AI model debate, weighing democratization and research benefits against misuse risks.
more → Security Boulevard, Tuesday, October 13th, 2026
Local models, certificate-pinned desktop apps and home connections hide AI use from proxies, so monitoring must move to the device.
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Computer Weekly, Tuesday, October 13th, 2026
ABN Amro, Santander, Axa, ING and AMD describe how they force AI projects to show measurable business value.
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VentureBeat, Monday, October 12th, 2026
Tool calling makes single actions deterministic but leaves the agent's decision layer probabilistic and the tool boundary ungoverned.
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AiThority, Monday, October 12th, 2026
As AI agents gain autonomy, enterprises need a supervisory layer that judges AI behavior, not just uptime and latency.
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TechZone360, Monday, October 12th, 2026
Scaling Claude beyond pilots depends on clean data, governance and fitting AI into the work employees already do.
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SiliconANGLE, Sunday, October 11th, 2026
Data gravity creates a compounding hidden cost in centralization-first AI architectures that surfaces 12 to 24 months in.
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Security Boulevard, Sunday, October 11th, 2026
Companies are using AI recommendation poisoning to bias assistants, a risk that falls between marketing and security teams.
more → CSO Online, Friday, October 9th, 2026
Lean security teams should apply AI where critical business outcomes are delivered inconsistently, not by tool category.
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AI Business, Friday, October 9th, 2026
New Western open-weight models give regulated enterprises an alternative to Chinese models, with new responsibilities.
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CIO.com, Friday, October 9th, 2026
Executives cutting engineering headcount over AI misread the job; demand for engineers who steward AI-built code will grow.
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