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A.I agent

A collection of 19 posts

Building an AI Research Agent That Actually Synthesizes (Not Just Summarizes)

Building an AI Research Agent That Actually Synthesizes (Not Just Summarizes)

AI research agents are getting very good at finding information. They can search dozens of websites, extract passages, summarize papers, compare documents, and generate polished reports in minutes. But finding and summarizing information is not the same as doing research. A system that summarizes ten sources may simply be compressing

Why Nebula Block Is Leading the Next-Gen AI Infrastructure Wave

AI Infrastructure

Why Nebula Block Is Leading the Next-Gen AI Infrastructure Wave

The first generation of AI infrastructure was built by retrofitting general-purpose cloud for AI workloads: rent a GPU from a hyperscaler, bolt on some MLOps tooling, and hope the compliance story holds up later. The next generation looks different — infrastructure built AI-native from the ground up, with sovereignty, agentic orchestration,

Why Agentic AI Is Replacing Point Solutions Across the Enterprise Stack

Why Agentic AI Is Replacing Point Solutions Across the Enterprise Stack

Quick answer: Agentic AI is replacing point solutions because a single AI agent can now execute multi-step workflows across systems that previously required separate, specialized software for each step — scheduling, research, data entry, reporting, and follow-up. Instead of buying five tools that each do one job, enterprises are deploying one

The 10 Best Open-Source AI Agent Frameworks Compared (2026 Edition)

best open-source

The 10 Best Open-Source AI Agent Frameworks Compared (2026 Edition)

Quick answer: In 2026, the open-source AI agent ecosystem is best understood as a combination of agent orchestration frameworks, SDKs, workflow engines, and interoperability protocols. The right choice depends less on whether a framework can call tools and more on how much control you need over state, execution, memory, observability,

12 Enterprise Workflows That Look Simple but Require Multi-Agent Coordination

12 Enterprise Workflows That Look Simple but Require Multi-Agent Coordination

On paper, many core business processes sound disarmingly straightforward: "Review this contract," "Onboard this vendor," or "Process this invoice." However, anyone who has attempted to map these processes end-to-end knows the reality. Behind every seemingly simple task lies a complex mesh of context switches,

How to Design an AI Agent That Feels Like a Team Member

How to Design an AI Agent That Feels Like a Team Member

Most people interact with Artificial Intelligence as a utility: a conversational search engine, a grammar fixer, or a quick code generator. You ask a question, receive an output, and return to your work. However, as AI shifts from passive tools to autonomous agents operating inside enterprise workflows, this transactional model

Why We Chose Go for an Enterprise AI Platform (Not Python)

Why We Chose Go for an Enterprise AI Platform (Not Python)

This is the question we get most often from other engineering teams: you're building an AI platform, so why isn't the core written in Python? Python owns the AI ecosystem — every major framework, every research paper's reference implementation, every model provider's primary

How We Protect IP When Deploying AI Software on Customer Infrastructure

How We Protect IP When Deploying AI Software on Customer Infrastructure

Shipping AI software directly onto a customer's servers — instead of hosting it yourself — solves real problems: data never leaves their network, latency drops, and procurement teams stop worrying about a third party touching regulated data. But it introduces a problem vendors don't face with SaaS: the

Single Binary Deployment: The Simplest Way to Ship an AI System

Single Binary Deployment: The Simplest Way to Ship an AI System

Most AI systems accumulate deployment complexity fast: a Python environment with a specific CUDA version, a dozen pip packages that need to match exactly, a model server, a vector database, maybe a queue. Getting that running reliably on someone else's infrastructure — a customer's data center, an

What "Air-Gapped AI" Actually Means in a Regulated Industry

What "Air-Gapped AI" Actually Means in a Regulated Industry

"Air-gapped" gets thrown around loosely in enterprise AI conversations, often as shorthand for "secure" or "on-prem." That looseness causes real problems, because in regulated industries - defense, critical infrastructure, healthcare, financial services handling classified or highly sensitive workloads — air-gapped has a precise meaning, and

Why Serverless Isn't Always Cheaper

Why Serverless Isn't Always Cheaper

"Serverless is cheaper because you only pay for what you use" is one of those pitches that's true often enough to become conventional wisdom — and wrong often enough to blow up a budget. The honest answer is: it depends entirely on your traffic shape, workload type,

The AI Audit Trail: Designing Model Lineage for Regulated Enterprises

The AI Audit Trail: Designing Model Lineage for Regulated Enterprises

When a regulator asks "why did the model make this decision," a screenshot of a dashboard is not an answer. In banking, insurance, healthcare, and other regulated industries, the question that actually gets asked is broader and harder: which model made this decision, on which version of which

The Hidden Costs of Managed Inference APIs Nobody Mentions Upfront

The Hidden Costs of Managed Inference APIs Nobody Mentions Upfront

Managed inference APIs look simple on the pricing page. Pay per token, no infrastructure to manage, done. Then your usage scales, and the bill starts telling a different story. The hidden costs of managed inference APIs rarely show up in the headline rate — they show up in the fine print,

H100 vs B300: When should enterprises upgrade?

H100 vs B300: When should enterprises upgrade?

Every GPU generation launch comes with the same pitch: newer is faster, so upgrade now. That's not wrong, exactly. But it's not the right question either. The real question in an H100 vs B300 decision isn't "which card is better" — it'

Bill 25 Decoded: How Canadian LLM Deployments Must Adapt to Stay Compliant

Bill 25 Decoded: How Canadian LLM Deployments Must Adapt to Stay Compliant

Regulatory scrutiny on artificial intelligence is no longer theoretical. In Canada, Quebec's Bill 25 has moved from policy to active enforcement — and the organizations that assumed compliance was primarily a legal team problem are finding out, the hard way, that it was an infrastructure problem all along. The

Why Data Residency Does Not Equal Data Sovereignty

Why Data Residency Does Not Equal Data Sovereignty

Every week, another Canadian bank, insurer, or law firm hears the same promise from a US cloud sales rep: "Your data stays in Canada." It’s technically true. It’s also legally meaningless. As agentic AI moves into core workflows—adjudicating claims, drafting contracts, and screening financial transactions—

Why Canadian AI Companies Should Build on Nebula Block

Why Canadian AI Companies Should Build on Nebula Block

Every Canadian company scaling an enterprise AI product arrives at the same crossroads: your stack works, your product is ready, and your first serious enterprise deal is finally on the table. Then legal review starts — and everything freezes. The question that kills the deal isn't about your model&