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Insights on agentic AI, from agent architectures and LLM infrastructure to enterprise deployment and developer tooling. Our team shares practical guides on building AI agents, optimizing model pipelines, and scaling AI systems in production.
Written for CTOs, developers, AI engineers, and technical leaders who are building or deploying agentic AI. Each article includes actionable takeaways grounded in real-world implementation.
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Building, deploying, and managing autonomous AI agents for workflow automation, customer support, internal operations, and more.
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Showing 73-96 of 982 posts
Open Weights Took 62% of the Tokens and Under 9% of the Spend
Vercel's AI Gateway put open-weight models at 62% of token volume in late August, up from 11% in April — while closed models still took roughly two-thirds of the spend. That split is not a contradiction, it is what a correctly routed AI estate looks like, and it is only available if switching models is a config change.
Thomson Reuters Spent $40M. The Training Run Cost $450K.
Thomson Reuters built its own legal and tax model on Alibaba's open-weight Qwen 3.5, trained on Westlaw and Practical Law content. The widely quoted numbers are $40M over two years and a $450K final training run — and the gap between them is the actual lesson, because 99% of the cost was not the compute.
The Chat Window Breaks at Five Agents. CanvasTTY Shows What's Next.
CanvasTTY arranges live terminals and AI-agent CLI sessions on an infinite canvas instead of in tabs, and zooms out to readable summaries rather than tiny noise. It is a developer tool, but it demonstrates the interface problem every organization running concurrent agents is about to hit: a linear transcript cannot show you five things at once.
When Avatar Video Is MIT-Licensed, Governance Is the Product
Meituan's LongCat-Video-Avatar 1.5 turns one portrait and an audio track into stable talking video under an MIT licence. Once generation is free and self-hostable, the scarce thing is no longer the model — it is a defensible record of whose likeness was used, who approved it, and what was produced.
Three Deployment Paths, One Codebase: Where Lock-In Actually Starts
ibl.ai documented three deployment paths for apps built on the platform — platform-hosted, your own container, or the App Store and Google Play — from a single codebase, all MIT-licensed and public. The reason this matters is that the number of exits a platform gives you is the most honest measure of lock-in available before you commit.
99% Plan to Deploy AI Agents. 9% Have. The Gap Is Not the AI.
A August 2026 survey found 99% of companies plan to put AI agents into production and only 9-14% have fully done so. The blocker is rarely model capability — it is that an agent needs a machine-readable account of how work actually happens, and most organizations have never written one down.
Revolut Built Its Own Foundation Model. Most Banks Can't.
Revolut launched a dedicated AI research lab on 25 August 2026 built around PRAGMA, a foundation model pre-trained on its own banking event sequences. It is the clearest signal yet that leading financial institutions are becoming AI companies rather than buying AI tools — and a useful reminder that the thing making it work is proprietary data plus an owned stack, not the model architecture.
GLM-5.3-Flash: Why a 4.44x Smaller KV Cache Changes Self-Hosting
Zhipu confirmed the anonymous 'Ox Alpha' model was GLM-5.3-Flash and released the weights: 320B total, 18B active, tying Claude Opus 4.8 on the Artificial Analysis index. The headline is the benchmark, but the number that matters for anyone self-hosting is the 4.44x KV-cache reduction — because KV cache, not parameter count, is what caps concurrent users per GPU.
Healthcare AI Should Start in the Billing Office, Not the Exam Room
Roughly 65% of denied healthcare claims are never appealed, while 54% of the ones that are get overturned. That gap is the highest-ROI AI deployment in healthcare, and it sits in the revenue cycle rather than at the point of care — but only if the PHI architecture survives a security review.
What the UK-Ukraine AI Declaration Actually Says About Sovereignty
The UK and Ukraine signed an AI partnership on 24 August 2026. It is a non-binding declaration about sharing battlefield data, not a sovereignty mandate — and reading it accurately matters more for government AI buyers than the headline does. What the document commits to, what it does not, and what India's DRONA 2.0 shows about sovereignty that is already operational.
Self-Hosted LLM Providers: Ollama vs vLLM vs TGI vs LocalAI
A practical guide to the self-hosted LLM serving stack — Ollama, vLLM, llama.cpp, Hugging Face TGI, LocalAI, and Open WebUI — what each one is actually for, the hardware each needs, and what you still do not own once the runtime is running.
Alibaba's ANOLISA Moves Agent Infrastructure Into the Operating System
Alibaba Cloud open-sourced ANOLISA, an agent-first Linux distribution that treats context compression, sandboxing and agent observability as operating-system services rather than application features. Here is what it actually ships, what the OS layer can and cannot own, and why the pattern favors organizations that own their stack.
The Agent-First Campus: Why Universities Are Buying an AI Operating System, Not Chatbots
Universities that moved past chatbots are not deploying a better chatbot — they are deploying a network of purpose-built agents wired into the SIS, LMS and CRM. The decision that determines whether it lasts is not which agents you build but whether you own the platform underneath them.
Longer Reasoning Can Make Models Worse — What That Means for Legal AI Routing
A multi-institution study found that extending a reasoning model's thinking time can reduce accuracy, with five distinct failure modes. For legal teams the consequence is concrete: brief drafting and contract extraction need different models, and paying for maximum reasoning on both is worse than routing.
Most Healthcare AI Pilots Never Reach Production — It Is an Architecture Problem
Roughly four in five healthcare AI pilots never reach production, and the cause is rarely the model. What separates the survivors is architecture: structured outputs, deterministic fallbacks, domain-specific evaluation and audit-complete observability — none of which a demo needs and all of which production requires.
OpenViking's Real Number Isn't 91%. It's AGPL-3.0.
ByteDance's OpenViking cuts agent token use by 34–91% and is at 32,900 GitHub stars. It is also AGPL-3.0, which is the fact enterprise architects need first — and the one every summary of the release leaves out.
Agent Skill Catalogs Are a Supply Chain. Who Signs Yours?
Enterprise teams have stopped asking how to deploy an agent and started asking who is allowed to publish a skill. NVIDIA's verified skill pipeline treats agent capabilities as signed software artifacts — which makes the catalog a supply chain, and raises the question of who holds the signing key.
Sovereign AI Is Now Procurement Policy, Not Rhetoric
France's Ministry of the Armed Forces signed a framework agreement with Mistral in January 2026, and Nigeria's National Digital Cloud Policy scopes sovereignty to government and regulated data. Sovereign AI has moved from speeches into contracts — and the contract terms are where it succeeds or fails.
DRONA 2.0: A Military College Replaced Its Custom GPT
India's Defence Services Staff College launched DRONA 2.0 on 18 August 2026, moving from a customised GPT to Sarvam-105B on a GPU server inside its own secure network. The 14-month migration is the clearest public template yet for how an institution goes sovereign on AI.
Healthcare AI Fails at the Information Layer, Not the Model
HIPAA's minimum necessary standard is a retrieval requirement, not a policy one. Most clinical AI enforces it at display time, which is too late — and it is why healthcare AI stalls at the information layer.
Decade-Long Compute Bets Face Two Opposite Curves
Frontier training costs are rising while the cost of a fixed capability has fallen roughly 1,000x in three years. Any decade-long AI infrastructure bet has to survive both curves, and they point in opposite directions.
The Multilingual Gap Is the Healthcare AI Access Barrier
IISc's SPIRE Lab released SraVaani under MIT — speech recognition for 65 Indian languages, 40+ of which no commercial system officially supports. Language coverage is an infrastructure choice, not a feature request.
Universities Pay Per Seat for a Runtime That's Now Free
The agent runtime went free this month from both DeepSeek and Microsoft. Universities still paying per student for AI assistants should ask what the per-seat fee is now buying.
The Agent Runtime Just Commoditized. Now What?
DeepSeek Harness has passed 191,000 GitHub stars under MIT, Microsoft's Agent Harness reached GA, TrueForge is MIT, and Block open-sourced its Berd agent workspace under Apache 2.0. The agent loop is free — so value moves to what you build on it, where you run it, and who governs what plugs into it.