Agents

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Anatomy of an AI agent knowledge base
Anatomy of an AI agent knowledge base
For AI agents, a knowledge base fuels fast and accurate responses and enables complex reasoning. We asked the experts how to build one.
·infoworld.com·
Anatomy of an AI agent knowledge base
Lindy signup
Lindy signup

With Lindy, you can build AI agents and apps in minutes simply by describing what you want in plain English. From inbound lead qualification to AI-powered customer support and full-blown apps, Lindy has hundreds of agents that are ready to work for you 24/7/365.

·chat.lindy.ai·
Lindy signup
Orgo - Computers for AI Agents
Orgo - Computers for AI Agents
  • Virtual computers for AI agents. Let them create files, browse the web, and install or use any desktop app.
·orgo.ai·
Orgo - Computers for AI Agents
Runable
Runable
A general agent for slides, websites, podcasts, videos—everything.
·runable.com·
Runable
Mesa – Senior-level code review
Mesa – Senior-level code review
A multi-agent system to understand your codebase for senior-level code reviews.
·mesa.dev·
Mesa – Senior-level code review
Agentic AI and Security
Agentic AI and Security
Commentary on Agentic AI and Security by Stephen Downes. Online learning, e-learning, new media, connectivism, MOOCs, personal learning environments, new literacy, and more
·downes.ca·
Agentic AI and Security
I've worked in AI for decades. Agentic AI will irreversibly change our workforce whether enterprises like it or not | Fortune
I've worked in AI for decades. Agentic AI will irreversibly change our workforce whether enterprises like it or not | Fortune
For Superhumans and their AI teammates to thrive, enterprises must build robust data foundations and memory systems. This means capturing and protecting an organization’s collective intelligence — not just data, but the decisions, judgment, intuition, and know-how that usually live in people’s heads. It’s a bit like creating a Pensieve for the enterprise – a living memory any AI agent can draw from. We call these context cartridges and knowledge capsules.
For Superhumans and their AI teammates to thrive, enterprises must build robust data foundations and memory systems.  This means capturing and protecting an organization’s collective intelligence — not just data, but the decisions, judgment, intuition, and know-how that usually live in people’s heads. It’s a bit like creating a Pensieve for the enterprise – a living memory any AI agent can draw from. We call these context cartridges and knowledge capsules.
·fortune.com·
I've worked in AI for decades. Agentic AI will irreversibly change our workforce whether enterprises like it or not | Fortune
Manus: Hands On AI
Manus: Hands On AI
Agentic system with faster task completion, coding
·manus.im·
Manus: Hands On AI
Home | Scorecard
Home | Scorecard
Evaluate, Optimize, and Ship AI Agents
·scorecard.io·
Home | Scorecard
Compyle - The coding agent that asks before it builds
Compyle - The coding agent that asks before it builds
Stop wasting time on 20-minute builds that do the wrong thing. Compyle keeps you in the driver's seat—planning together first, then asking when anything's unclear.
The coding agent that asksbefore it builds
·compyle.ai·
Compyle - The coding agent that asks before it builds
Agentic AI in Education: State of the Art and Future Directions | IEEE Journals & Magazine | IEEE Xplore
Agentic AI in Education: State of the Art and Future Directions | IEEE Journals & Magazine | IEEE Xplore
Agentic AI systems are self-contained, goal-based entities designed to operate autonomously with little human intervention and dynamically respond to shifting contexts. This survey considers their transformative potential in education, ranging from intelligent tutoring systems and adaptive test-taking platforms to autonomous learning companions that can provide customized guidance and assistance. We provide an organized overview of the state of the field, classify existing and developing uses and assess their pedagogical worth, benefits and trade-offs
Agentic AI systems are self-contained, goal-based entities designed to operate autonomously with little human intervention and dynamically respond to shifting contexts. This survey considers their transformative potential in education, ranging from intelligent tutoring systems and adaptive test-taking platforms to autonomous learning companions that can provide customized guidance and assistance. We provide an organized overview of the state of the field, classify existing and developing uses and assess their pedagogical worth, benefits and trade-offs
·ieeexplore.ieee.org·
Agentic AI in Education: State of the Art and Future Directions | IEEE Journals & Magazine | IEEE Xplore
Real AI Agents and Real Work
Real AI Agents and Real Work
The race between human-centered work and infinite PowerPoints
·oneusefulthing.org·
Real AI Agents and Real Work
Google Opal [Experiment]
Google Opal [Experiment]
Build, edit, and share AI mini-apps with natural language
·opal.withgoogle.com·
Google Opal [Experiment]
How to Get Ahead of 99% with AI Agents - YouTube
How to Get Ahead of 99% with AI Agents - YouTube
Episode 76: What actually makes something a real "AI Agent"—and how close are we to AI handling complex work entirely on its own?*Want our guide to master AI...
·m.youtube.com·
How to Get Ahead of 99% with AI Agents - YouTube
Anthropic IBM Bible | Alex Cinovoj | 51 comments
Anthropic IBM Bible | Alex Cinovoj | 51 comments
Stop shipping agents like they’re apps. They’re self-governing code touching live systems. IBM × Anthropic is the signal: enterprise agents are here, and governance just became table stakes. Here’s the playbook I’m seeing (10 pieces you can ship, certify, and scale): ✅ what are ai agents? adaptive systems that reason, act, and learn with tools, not static chat apps. ✅ agentic enterprise embed agents into ops so decisions, workflows, and automations improve every run. ✅ ADLC (agent development lifecycle) devsecops for agents: design → sandbox → red-team → certify → deploy → monitor → retire. ✅ enterprise considerations tie use cases to ROI, controls, and regs; write the business case before the prompt. ✅ observability & ops beyond uptime: track behavior drift, tool errors, chain depth, reasoning quality, and rollback readiness. ✅ agent security defend against prompt injection, data leakage, privilege escalation; least-privilege tools with signed calls. ✅ governance: test, certify, catalog treat agents like services: pre-release evals, attestations, lineage, and an internal marketplace. ✅ MCP servers lifecycle model context protocol as a first-class surface: auditable, scoped, and versioned integrations. ✅ reference architecture hybrid stack that separates knowledge (RAG), capability (tools), policy (guards), and memory (state). ✅ voice of the customer & use cases ship real deployments (healthcare, telecom, finance) with before/after metrics, not vibes. If you’d word any of this differently, I’m all ears, drop your version and I’ll pin the clearest take. IBM/Anthropic folks, feel free to sharpen this for the operators in the trenches. Bottom line: this isn’t “labs” anymore. If you can’t test it, certify it, and roll it back in minutes, you shouldn’t run it in production. Follow Alex for operator-grade AI agents you can copy, and repost to put this in front of one teammate who owns your next deployment. Thanks Andreas Horn for sending this over. | 51 comments on LinkedIn
·linkedin.com·
Anthropic IBM Bible | Alex Cinovoj | 51 comments