Practical articles on enterprise AI, agents, RAG, and open source automation.
Skip the CLI and HTTP layer entirely. Import OE Runtime as a library and call runAgent() directly from your Node.js application.
Call OE Runtime agents from Python with requests or httpx. Works with FastAPI, Django, Airflow, and plain scripts. No Python SDK needed.
Monitor ETH on Optimism, track RetroPGF grant wallets, and audit DAO multi-sig treasury balances with an AI agent. Two files, one command.
Query ETH balances on Coinbase's Base L2 and monitor onchain consumer app treasuries, creator wallets, and social token flows with an AI agent.
Monitor AVAX balances, track validator staking rewards, and audit enterprise subnet treasury wallets on Avalanche C-Chain with an AI agent.
Monitor ETH balances on Arbitrum One, analyse institutional DeFi wallet flows, and track GMX trading activity with an AI agent. No code required.
Query MATIC balances, track NFT creator wallets, and monitor enterprise loyalty programme wallet flows on Polygon with an AI agent.
Monitor BNB balances on Binance Smart Chain, track PancakeSwap liquidity wallets, and analyse token launch activity with a YAML AI agent.
Query ETH wallet balances on Ethereum mainnet for DeFi treasury management, AML compliance checks, and DAO spending audits — using a two-file AI agent.
Your agent pauses at a manual checkpoint and posts a message in Slack. Reply yes to continue, no to cancel — right inside your channel.
Create a Slack app, configure the webhook, set up bot events, and your entire OE agent library is live inside Slack — no backend code required.
Your agent hits a manual checkpoint and sends you a Telegram message. Reply yes to continue, no to cancel — from anywhere, on any device.
OE MCP automatically records every connector tool call to a log file. Use log_list and log_clear to audit, debug, and monitor what your AI is doing — no setup required.
One folder, one project file, one npx command — your Telegram bot is live, running whichever agent you ask for, with zero backend code.
Run two or more AI agents in sequence from a single YAML file. Auto chains fire immediately; manual chains pause for your approval. Works in CLI, HTTP, and MCP modes.
Run your AI agent as an HTTP server. One command starts it, then any app, script, or webhook can trigger it via REST API — no CLI access needed.
No internet. No API key. No cost per token. Run a full end-to-end AI agent pipeline on your own machine using Ollama — two files, one command, zero cloud dependency.
Use the shell connector to let an agent execute scripts directly on the local machine. No SSH, no HTTP wrapper — two files and the agent handles the rest.
Every field in the three files that power an OE Runtime project — annotated examples, a complete field table, and a full working multi-agent project.
Build and run your first AI agent in under 5 minutes. Two files, one command — no Python, no LangChain, no boilerplate. Just a working agent that returns a result.
OE MCP v1.6.6 ships a new run_agent tool — trigger any OE Runtime YAML agent directly from Claude Code, Cursor, or Windsurf. No terminal. No extra setup. Just ask.
Give your AI assistant a long-term memory that survives across sessions. OE MCP's built-in memory tools let Claude remember database hosts, release rules, project context, and more — forever.
Complete series covering all 21 connector types — databases, APIs, cloud storage, email, messaging, IoT, and media generation. Each guide includes the full YAML, oe-config.json, and copy-paste run commands.
Run multiple web searches, synthesize findings, and produce structured research reports with citations — all from a YAML agent and OE Runtime.
Generate cinematic short video clips from text prompts using an AI agent and Runway. Includes YAML config, API key setup, and run commands for all platforms.
Automatically run disk, memory, CPU, and service checks on remote Linux servers via SSH. YAML config and run commands for all platforms.
Explore PostgreSQL schemas, run SELECT queries, identify data quality issues, and get plain-English summaries — all from a YAML agent.
Convert any text to natural-sounding speech audio using an AI agent and ElevenLabs. Includes YAML config, voice settings, and run commands for all platforms.
Make GET and POST requests to any REST API, process responses, and produce structured summaries — all from a YAML file. Config and run commands included.
Automatically fetch open GitHub issues and PRs, flag stale items, identify unreviewed PRs, and produce a repository health report using OE Runtime.
Extract text from images using Azure Vision OCR, classify document type, and structure key fields automatically. YAML config and run commands included.
Query MongoDB collections, inspect document schemas, and identify data quality issues automatically using OE Runtime.
Generate original background music tracks from text descriptions using an AI agent and Suno. YAML config, API key setup, and run commands for all platforms.
Check Kafka topic status, publish test events, and verify delivery automatically using an AI agent and OE Runtime.
Subscribe to MQTT sensor feeds, detect threshold breaches, and publish control commands automatically using OE Runtime.
Introspect a GraphQL schema, run queries and mutations, and summarize results automatically — no hand-written queries required.
List, audit, and manage AWS S3 bucket contents with an AI agent. Identifies large files, classifies by type, and flags archiving candidates.
Automatically scan unread Gmail messages and send professional replies using the Gmail REST API and OAuth. Full YAML config and run commands included.
Automate LDAP and Active Directory user searches, account health checks, and compliance audits using OE Runtime.
Automatically list, audit, and organise Google Drive files using an AI agent and OE Runtime. Includes OAuth setup and run commands for all platforms.
Query Ethereum wallet balances, transaction history, and smart contract state from the command line using OE Runtime. YAML config and run commands included.
Auto-craft prompts and generate high-quality images from text using OE Runtime. Includes YAML config, oe-config.json, download links, and run commands for Windows, macOS, and Linux.
How to build a private ChatGPT for your business — one that runs on your own infrastructure, answers from your own data, and never sends anything to a third-party server.
How manufacturers use AI to capture institutional knowledge, reduce downtime, streamline compliance documentation, and give plant floor workers instant access to technical expertise.
AI eliminates the 60% of a recruiter's day that is administrative busywork — CV screening, job descriptions, outreach, ATS updates — so consultants can focus on placements.
A practical breakdown of the four mechanisms through which AI delivers measurable cost reductions — and how to calculate the ROI before you invest.
How to build an AI-powered knowledge base that actually gets used: what to include, how to structure it, how to connect live data sources, and how to keep it current over time.
Enterprise-grade AI is now accessible to any business willing to spend a weekend setting it up. Here is where to start, what to expect, and how to avoid the common mistakes.
The most valuable AI deployment is often internal: an AI assistant that knows your policies, processes, and data so employees get instant answers without bothering a colleague.
When should an AI agent run on-demand, on a schedule, or in response to an event? Understanding trigger strategies is one of the most important agent design decisions.
Vector databases, embeddings, semantic search — how AI talks to data stores, what the difference is from traditional databases, and what it means for your architecture.
Agentic AI in production, self-hosted as the default, open source closing the gap — the seven trends defining how organizations deploy and govern AI in 2026.
From "AI will replace all jobs" to "you need a massive budget to start" — the most damaging AI misconceptions holding businesses back, and what is actually true.
AI agents handle CV screening, interview scheduling, onboarding, policy Q&A, and offboarding — leaving HR professionals free for the work that actually requires human judgment.
How AI changes workflow automation — from brittle scripts to intelligent pipelines that handle variation, make decisions, and adapt without developer intervention.
How law firms are deploying AI for contract review, legal research, client intake, and document drafting — with clear-eyed guidance on where human oversight is non-negotiable.
Financial services firms are using AI to automate reporting, monitor compliance, accelerate due diligence, and improve client communication — on infrastructure they control.
AI assistants answer questions. AI agents complete tasks. Understanding the difference helps you deploy the right tool for each problem.
RAG is the technique that makes enterprise AI accurate. Instead of relying on training data, it retrieves information from your own documents before generating an answer.
The AI tools that deliver real business value in 2026 — not a list of hype, but the specific capabilities that save time, reduce costs, and compound over time.
Healthcare generates more data per employee than almost any other industry. Self-hosted AI handles documentation, admin workflows, and internal knowledge search — without patient data leaving your network.
Sales teams spend less than 30% of their time actually selling. AI agents eliminate the research, admin, and follow-up overhead that fills the rest.
An AI assistant is a conversational interface connected to a knowledge base and a language model. Here is exactly how it works and what makes it different from a chatbot.
An AI agent does things — it doesn't just answer questions. A clear explanation of what AI agents are, how they work, and where they deliver real business value.
AI connected to your CRM can research prospects, draft personalised outreach, update records, flag churn risk, and generate post-call summaries — automatically and at scale.
A concrete six-month roadmap for small and mid-size businesses to move from first AI deployment to organisation-wide adoption — with specific milestones and metrics.
The enterprise AI use cases that consistently deliver measurable returns — not hypothetical future potential, but what is working in production deployments today.
An LLM knows nothing about your organisation. RAG connects powerful language models to your private data — and for enterprises, it is not optional. Here is how to get it right.
A practical guide to what enterprise AI means, how it differs from consumer AI tools, and why it matters for your business.
Employees spend nearly 20% of their week searching for information that already exists. Enterprise knowledge search makes your documents instantly queryable in natural language.
Full automation is not always the goal. Human-in-the-loop AI keeps humans in control of high-stakes decisions while automating everything around them.
A single AI agent handles a focused task well. Multi-agent systems coordinate several agents working in sequence or in parallel to complete complex, multi-step workflows.
DevOps teams manage complexity that scales faster than headcount. AI agents handle monitoring, triage, and routine remediation — so engineers can focus on building, not babysitting infrastructure.
Most customer support teams handle the same issues repeatedly at high volume. AI agents handle triage, draft responses, and resolve common issues — so humans can focus on the complex ones.
Private AI keeps your data inside your own infrastructure. Public AI sends it to a shared cloud. Here's what that means for your business.
Enterprise AI is moving from experimentation to infrastructure. Here's what the next five years look like — and what to do now.
Buying AI tools without a strategy is like buying a fleet of lorries without a logistics plan. Here's how to build one that compounds.
ChatGPT is a consumer tool. Enterprise AI is a private platform connected to your business data. Here's exactly how they differ.