Understanding AI Computer Use Agents

As AI evolves from automation to true autonomy, 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 isn’t optional — it’s foundational. Whether you're building a GenAI product, orchestrating autonomous workflows, or designing agentic RAG pipelines, the core question remains: 𝘞𝘩𝘢𝘵 𝘵𝘺𝘱𝘦 𝘰𝘧 𝘢𝘨𝘦𝘯𝘵 𝘢𝘳𝘦 𝘺𝘰𝘶 𝘥𝘦𝘴𝘪𝘨𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘴𝘺𝘴𝘵𝘦𝘮? To help you think more clearly about this, I created a visual on the 𝟴 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 — from simple rule-followers to highly adaptive, reasoning-based LLM-powered agents. Here’s a breakdown of the intelligence spectrum: ↳ 𝗦𝗶𝗺𝗽𝗹𝗲 𝗥𝗲𝗳𝗹𝗲𝘅 𝗔𝗴𝗲𝗻𝘁𝘀 – React to current input. No memory. Think: Thermostats or Rule-based chatbots.   ↳ 𝗠𝗼𝗱𝗲𝗹-𝗕𝗮𝘀𝗲𝗱 𝗥𝗲𝗳𝗹𝗲𝘅 𝗔𝗴𝗲𝗻𝘁𝘀 – Track internal state and context. Used in bots that respond based on past inputs.   ↳ 𝗚𝗼𝗮𝗹-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀 – Don't just act — they pursue objectives. Ideal for pathfinding and planning tasks.   ↳ 𝗨𝘁𝗶𝗹𝗶𝘁𝘆-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀 – Choose the best option among many using utility functions. Common in recommendation systems.   ↳ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀 – Improve performance over time. Learn from feedback.   ↳ 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 – Think, plan, act, and adapt — all without human oversight.   ↳ 𝗟𝗟𝗠-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀 – Leverage the reasoning power of large language models to simulate human-like cognition.   ↳ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 – Multiple agents working together to solve problems collaboratively or competitively. Think swarm intelligence or agentic RAG. As we move toward 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀, we’re shifting from 𝘵𝘰𝘰𝘭𝘴 𝘵𝘩𝘢𝘵 𝘢𝘴𝘴𝘪𝘴𝘵 to 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘵𝘩𝘢𝘵 𝘤𝘰-𝘤𝘳𝘦𝘢𝘵𝘦 — digital workers that can collaborate, reason, and even negotiate. If you’re building in AI, this isn’t just theory — it’s design strategy.

If you’re getting started with AI agents, this is for you 👇 I’ve seen so many builders jump straight into wiring up LangChain or CrewAI without ever understanding what actually makes an LLM act like an agent, and not just a glorified autocomplete engine. I put together a 10-phase roadmap to help you go from foundational concepts → all the way to building, deploying, and scaling multi-agent systems in production. Phase 1: Understand what “agentic AI” actually means → What makes an agent different from a chatbot → Why long-context alone isn’t enough → How tools, memory, and environment drive reasoning Phase 2: Learn the core components → LLM = brain → Memory = context (short + long term) → Tools = actuators → Environment = where the agent runs Phase 3: Prompting for agents → System vs user prompts → Role-based task prompting → Prompt chaining with state tracking → Format constraints and expected outputs Phase 4: Build your first basic agent → Start with a single-task agent → Use UI (Claude or GPT) before code → Iterate prompt → observe behavior → refine Phase 5: Add memory → Use buffers for short-term recall → Integrate vector DBs for long-term → Enable retrieval via user queries → Keep session memory dynamically updated Phase 6: Add tools and external APIs → Function calling = where things get real → Connect search, calendar, custom APIs → Handle agent I/O with guardrails → Test tool behaviors in isolation Phase 7: Build full single-agent workflows → Prompt → Memory → Tool → Response → Add error handling + fallbacks → Use LangGraph or n8n for orchestration → Log actions for replay/debugging Phase 8: Multi-agent coordination → Assign roles (planner, executor, critic) → Share context and working memory → Use A2A/TAP for agent-to-agent messaging → Test decision workflows in teams Phase 9: Deploy and monitor → Host on Replit, Vercel, Render → Monitor tokens, latency, error rates → Add API rate limits + safety rules → Setup logging, alerts, dashboards Phase 10: Join the builder ecosystem → Use Model Context Protocol (MCP) → Contribute to LangChain, CrewAI, AutoGen → Test on open evals (EvalProtocol, SWE-bench, etc.) → Share workflows, follow updates, build in public This is the same path I recommend to anyone transitioning from prompting → to building production-grade agents. Save it. Share it. And let me know what phase you’re in, or where you’re stuck. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

While everyone's talking about AI agents, most developers and researchers are missing out on the most comprehensive collection of computer use frameworks, papers, and tools ever assembled. Awesome Agents for Computer Use is a curated repository documenting the recent rapid progress of AI agents that can autonomously control computers through clicks, keystrokes, and API calls. From Anthropic's Claude Computer Use to Microsoft's OmniParser and Self-Operating Computer framework, it covers the entire landscape of computer control agents. It features: * Research papers - featuring 30+ recent publications on GUI agents, from foundational models to safety considerations * Open-source frameworks - documenting practical implementations like AutoGen, Browser Use, and OpenInterpreter * Commercial solutions - tracking industry developments from major players like Anthropic and emerging startups The rise of computer-controlling AI agents marks a pivotal shift in human-computer interaction. As these systems mature, we're moving towards a future where AI assistants won't just give advice - they'll directly help us accomplish complex tasks across applications and platforms (I wrote more about this topic here https://lnkd.in/gFBaaS_i) Repo https://lnkd.in/gTwTX5Tb