Whitepapers, field notes, and long essays on agent architectures, evaluation, verification, and the practical engineering of accountable AI operations. Written by the people building AGNT.
The model thinks. The harness works. A plain-language breakdown of what a language model does, what an agent harness does, how one real task splits between them, and why the harness decides whether super intelligence ships results or just talks.
Read it →The model thinks. The harness works. A plain-language breakdown of what a language model does, what an agent harness does, how one real task splits between them, and why the harness decides whether super intelligence ships results or just talks.
AI just got renamed "super intelligence." Whatever you call it, raw intelligence is only useful when something points it at real work, remembers what happened, asks before it acts and keeps a record. That something is the harness, and it is about to become the most important layer in the stack.
At the UN on September 22, 2026, President Trump said the United States will call artificial intelligence "super intelligence," or SI, in all official documents. Here is exactly what he said, where the word comes from, what changes, and what it means if you build with AI.
100 practical AI automation ideas with triggers, actions, outputs, human checkpoints, build components, effort, and risk.
100 AI agent tools grouped by capability, with each tool’s function, agent use case, constraint, and direct first-party source.
100 Claude Code skills and skill-bearing plugins, verified against Anthropic docs and public repos, with install and security notes.
100 MCP servers grouped by job, with direct source links, selection advice, setup examples, and security notes for AI agents.
100 copyable AI agent prompt templates with required inputs, output contracts, tool rules, stop conditions, and failure checks.
75 AI agent use cases with autonomy loops, tools, human controls, failure modes, risk tiers, and cited deployed examples.
50 AI agent frameworks compared by language, orchestration model, best fit, limits, deployment style, and primary documentation.
50 local and self-hosted AI agents compared by execution, model, storage, telemetry, platform, license, and practical limits.
50 open-source and source-available AI agents compared by license, deployment, capabilities, fit, limits, and source access.
Build one bounded research agent three ways, with tool schemas, budgets, approvals, retries, evaluations, tests, and security controls.
Sign in with the Claude plan you already pay for and run Claude models inside AGNT. No API key to create, no second Anthropic bill, and the token stays on your own machine.
AI's real shift is not that machines can make artifacts. It is that artifacts are becoming editable, executable systems. The next platform is not the renderer. It is the operating layer around the renderer.
Hermes Agent gives an autonomous worker a notebook of reusable procedures. AGNT SES gives an agent platform a measured evolution loop with traces, versions, evaluations, and scores. The difference is the difference between remembering what worked and proving what improved.
A comprehensive, research-backed guide to the 100 most powerful AI agent skills across 12 categories — from coding and design to security, finance, and full-stack automation. Curated from 5,200+ community skills, GitHub repositories, enterprise deployments, and expert sources.
Anthropic hit $30B ARR and overtook OpenAI. Claude Mythos was deemed too dangerous to release. Sam Altman's home was firebombed. Meta launched Muse Spark. Google dropped Gemma 4. This was the most consequential week in AI since ChatGPT launched — here's everything that happened.
How AGNT's evolution system observes every execution, extracts typed insights, and rewrites its own agents, workflows, and skills to compound knowledge over time
A deep comparison of Nous Research's Hermes Agent and AGNT — from architecture and memory to skills, workflows, evolution loops, and the AGI-style iteration engine that sets AGNT apart.
Every learned parameter is a liability. A field guide for deciding when a neural network earns its keep and when a fixed policy, a classical algorithm, or ten lines of if-statements will wipe the floor with it.
Evolving a naive Run-Length Encoder through 10 generations of machine-guided optimization to achieve an 80.9% improvement in combined performance
A source-code-level technical comparison of OpenClaw (the unified messaging gateway), Hermes (the self-improving agent by Newest Research), and AGNT — the full-stack AI command center with visual workflows, multi-agent orchestration, and autonomous goal execution.
Step-by-step guide to connecting OpenAI Codex CLI to AGNT using device authentication, with troubleshooting tips and supported models
A technical comparison of Andrej Karpathy's Autoresearch — a 630-line autonomous ML research loop — against AGNT's SkillForge architecture for autonomous skill evolution across multi-agent systems.
A deep-dive technical comparison of Rhys Sullivan's Executor — a local-first sandboxed tool runtime for AI agents — against the AGNT platform's integrated tool, MCP, and workflow ecosystem.
A thorough technical evaluation of migrating AGNT's LLM provider system to @mariozechner/pi-ai. After quantifying maintenance costs, migration effort, ROI, and dependency risks, we recommend against migration and propose targeted improvements instead.
From single-agent loops to multi-agent orchestration — a deep dive into every architecture pattern powering today's most capable AI systems.
A comprehensive evaluation framework for AI agent platforms using gold standard datasets, trace-based scoring, and LLM-as-a-Judge evaluators.
A technical guide to designing and deploying autonomous AI agents using the AGNT API, covering endpoint integration, architecture patterns, and real-world implementation.
A biologically inspired cognitive memory framework modeled after the hippocampal-neocortical system for long-term reasoning and autonomous learning.
A Unified Architecture for Context, Memory, Reasoning, and Recursive Self-Improvement in Autonomous Intelligence Systems
How to achieve instant AI responses using persistent socket connections, multi-agent architectures, and the agent-sdk's bidirectional streaming—eliminating cold start delays entirely
Complete TypeScript/JavaScript reference for the Claude Agent SDK including installation, core functions, configuration options, tools, hooks, and migration guide
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Unedited words from the AGNT community — developers, operators, and first-timers who ran a real job and saw the receipt. Spelling preserved exactly as written.
“So far you’ve accomplished what AutoGPT did not for me: have actual workflows running, not on my own computer, in a non-containerized environment, that would otherwise require me to invest more time and effort setting up.”
“AGNT is the best orchestration harness I’ve come across. I recommend all of you try this out! This interactive demo is so impressive — just off scraping our website, Annie built this whole interactive page. Real-world use cases included.”
“After using a lot of different systems I can tell you the way AGNT works when people see it run is an eye opener. The key is to see it run as someone building.”
“On a high level the workflow builder seems great, and the UI is super nice. Really looking to accelerate my workflows and run more parallel agents.”
“I have a skill that I run after each workflow that patches the workflow skill — but this is automatic and the insights are really good! Cool.”
“I’m a normal user, no IT background — my job is nurse, haha. But I’m learning and want to go deeper into AI. AGNT works well for me.”
Authentic community feedback, preserved as written. Handles shown where a public testimonial was given; other members attributed by role and date to protect privacy. Not paid endorsements.