Autonomous AI Agents Guide

Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing! Happy agenting! 🤖

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.

The open-source AI agent ecosystem is exploding, but most market maps and guides cater to VCs rather than builders. As someone in the trenches of agent development, I've found this frustrating. That's why I've created a comprehensive list of the open-source tools I've personally found effective in production. The overview includes 38 packages across: -> Agent orchestration frameworks that go beyond basic LLM wrappers: CrewAI for role-playing agents, AutoGPT for autonomous workflows, Superagent for quick prototyping -> Tools for computer control and browser automation: Open Interpreter for local machine control, Self-Operating Computer for visual automation, LaVague for web agents -> Voice interaction capabilities beyond basic speech-to-text: Ultravox for real-time voice, Whisper for transcription, Vocode for voice-based agents -> Memory systems that enable truly personalized experiences: Mem0 for self-improving memory, Letta for long-term context, LangChain's memory components -> Testing and monitoring solutions for production-grade agents: AgentOps for benchmarking, openllmetry for observability, Voice Lab for evaluation With the holiday season here, it's the perfect time to start building. Post https://lnkd.in/gCySSuS3

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

AI Agents are task-specific, autonomous systems that integrate large language models with structured tools, APIs, and real-time data sources. They operate across domains such as cybersecurity, supply chain logistics, and healthcare by executing workflows that traditionally required human-in-the-loop decision making. These agents leverage vector databases, retrieval-augmented generation, and fine-tuned embeddings to enable contextual reasoning and dynamic response generation. As orchestration frameworks mature, multi-agent systems are increasingly capable of handling end-to-end processes like demand forecasting, patient triage, and adaptive tutoring with minimal supervision. The below chart shows just how broad their impact is: 1.🔹 IT & Security : Phishing filters, threat detection, patch suggestions 2.🔹Healthcare : Patient alerts, medical chatbots, symptom matching 3.🔹 Education : Flashcards, concept explainers, AI tutors 4.🔹 Sales & Marketing : Lead scoring, campaign ideas, email outreach 5.🔹Logistics : Fleet tracking, demand forecasting, inventory updates 6.🔹Manufacturing : Predictive maintenance, robotic control, energy monitoring 7.🔹 Research : Academic writing, data cleaning, topic expansion 8.🔹 Customer Support : FAQ bots, emotion detection, chat summaries 9.🔹 Smart Environments : Digital twins, voice commands, access control 10.🔹Ops Automation : Shift scheduling, system alerts, order tracking What used require significant manual effort, now takes a few smart agents. I believe it’s a great time to start exploring and experimenting in this space… #genai #aiagents #artificialintelligence

Everyone talks about AI agents. But few actually show useful workflows. In today's episode, Harish Mukhami actually builds an AI employee: He builds an AI CS agent in just 62 minutes. 📌 Watch here: https://lnkd.in/eKbay8tu Also available on: Apple: https://lnkd.in/eAEVwr3u Spotify: https://lnkd.in/eyt7agKj Newsletter: https://lnkd.in/e6KUXi_z Harish is the former CPO at LeafLink (valued at $760M) and Head of Product at Siri. Now, he is the CEO and founder of GibsonAI, which built the scalable database behind our AI agent. Here were my favorite takeaways: 1: Building an AI employee just took 62 minutes. Harish demonstrated creating a fully functional customer success agent using ChatGPT O3 Mini, Gibson AI, Cursor, and Crew AI. The system analyzes data, identifies churn risks, sends emails, and creates Jira tickets—all production-ready. 2: Follow a three-stage evolution for maximum adoption success. Start with dashboards for insights, move to AI recommendations with human approval, then progress to full automation. This builds organizational confidence while gradually removing humans from routine tasks. 3: Architecture planning upfront prevents weeks of technical debt later. Use reasoning models like O3 Mini to define data models and business logic before coding. This ensures clean integration with existing tools rather than building isolated prototypes. 4: Production infrastructure is becoming accessible to non-technical teams. AI-powered databases auto-provision environments, generate APIs, and handle scaling without DevOps knowledge. Gibson deployed production-grade infrastructure in <3 mins. 5: MCP protocols eliminate the need to context-switch between tools. Model Context Protocol connects databases to code editors, letting you manage everything through natural language. Complex workflows across multiple tools become simple prompts. 6: Multi-agent frameworks make sophisticated automation accessible to PMs. Crew AI abstracts complexity that normally requires engineering expertise. Define specialized agents and orchestrate them like managing a human team with clear handoffs. 7: Any information worker role can now be automated. The same framework applies to SDRs, recruiters, and executive assistants. If your job involves data analysis and action-taking, it's automatable. 8: The PM skillset is evolving faster than most teams realize. Product managers who can architect agent workflows and design human-AI handoffs will have exponential impact. Natural language is becoming the primary interface for building software. 9: Development timelines have compressed from quarters to hours. The combination of reasoning models, AI infrastructure, and agent frameworks represents the biggest productivity shift since cloud computing for resource-constrained product teams.

Building your first AI agent can feel overwhelming. There are so many tools, frameworks, and steps that it is easy to lose track of where to begin. To simplify the process, I created a 20-step roadmap that takes you from the idea stage all the way to launch and ongoing maintenance. Here’s how the journey looks: 1. Define the purpose of your agent with clear success metrics and use cases 2. Select the right development framework like LangChain, AutoGen, or CrewAI 3. Choose a language model such as GPT-4, Claude, or LLaMA 2 based on cost, performance, and needs 4. Outline the core capabilities and limits of your agent 5. Plan tool integrations, APIs, and databases your agent will need 6. Design the agent architecture including input handling, processing, and error management 7. Implement memory management for both short-term and long-term interactions 8. Create prompt templates that are structured and reusable 9. Add context injection for more accurate and personalized responses 10. Enable tool calling for real-world task completion 11. Equip your agent with multi-step reasoning and planning abilities 12. Apply safety filters to prevent harmful or biased outputs 13. Set up monitoring for accuracy, latency, and user feedback 14. Optimize for speed using caching, async calls, and model efficiency 15. Enable continuous learning through retraining, A/B testing, and feedback loops 16. Add multimodal capabilities like text, image, speech, and video 17. Personalize the experience based on user history and preferences 18. Plan deployment strategy across web, mobile, APIs, or on-device 19. Launch with a controlled rollout and proper support in place 20. Maintain and upgrade regularly to stay secure and relevant Every step builds on the previous one. Together, they form a structured path for turning ideas into real, functional AI agents. If you are experimenting with agentic AI or planning to build your first agent, this roadmap can be your starting point. Save it for future reference. ————- ✅ I post real stories and lessons from data and AI. Follow me and join the newsletter at www.theravitshow.com

Stop building single AI agents. Start building AI Agent teams. Google ADK's multi-agent patterns let you orchestrate specialized agents like a well-coordinated team. Single agents hit limits fast. They try to do everything and end up doing nothing well. Solution? Delegate tasks to multiple specialized agents that work together. 3 Multi-agent pattern you can build with Google ADK with opensource step-by-step code tutorials: 1. Sequential Agents: ↳ Execute agents one after another in strict order ↳ Perfect for business analysis pipelines ↳ Each step builds on the previous one Code tutorial 👉 https://lnkd.in/dySsFiNi 2. Loop Agents: ↳ Iterate until conditions are met ↳ Ideal for refinement and revision tasks ↳ Shared state across iterations Code tutorial 👉 https://lnkd.in/dxwa48NN 3. Parallel Agents: ↳ Run multiple agents concurrently ↳ Dramatically speeds up independent tasks ↳ Each agent writes to shared state safely Code tutorial 👉 https://lnkd.in/drn5yTHt The best part? It's 100% Open Source. Link to the GitHub repo with full Google ADK Crash Course in comments!

AI Agents - or shifting chat bots into do bots, is the next big thing AI development currently in the hype stage. This article discusses a responsible framework. Taking the leap from having generative AI to do simple tasks to exploring workflows is one step. But AI agents goes beyond that to automating a department or team workflow. That requires some readiness steps including: 1) Identify Repetitive Tasks for Automation: Identify routine and time-consuming tasks that AI agents can handle. These might be some of the simple tasks that you are using generative AI for right now. But you want to put those in the context of a whole workflow using process mapping. 2) Small Controlled Team or Dept. Pilot: Identify a pilot that is low-risk. Better places to start are on internal workflow processes. Identify a metric for success - time savings or work quality improvement? 3) Ensure Human Oversight: While AI agents can handle many tasks autonomously, it's crucial to maintain human oversight, especially for tasks requiring nuanced judgment or ethical considerations. These should be identified during process mapping. And, once the pilot is up and running, set up bias checks, audits, and steps to address issues. 4) Invest in Training and Development: Equip people with the necessary skills to work alongside AI agents. This includes training in prompting, data management, and understanding AI functionalities. Agents are not a pot-roast, set it and forget technology. They require preparation, planning, and monitoring. https://lnkd.in/gh5rXDfH

AI agents are taking over LinkedIn feed—but do you actually know how they work together? Think of them as an elite task force—but instead of humans, these are intelligent agents collaborating to plan, analyze, and execute tasks at scale. So… what would their org chart look like? I recently explored multi-agent design patterns, and here are the key takeaways: 🏗 1. The Assembly Line (Sequential Pattern) Like a factory, each AI agent completes a step before passing it down the line. 🔹 Example: Automating document processing Agent 1 extracts text Agent 2 summarizes key info Agent 3 identifies action items Agent 4 stores results in a database ✅ Best for: Clear, structured workflows with defined hand-offs. 🏢 2. The Manager & Sub-Agents (Hierarchical Pattern) A "boss" agent delegates tasks to specialized sub-agents. 🔹 Example: AI-powered business decision-making The Manager agent oversees the process Sub-agent 1 tracks market trends Sub-agent 2 analyzes customer feedback Sub-agent 3 monitors internal metrics ✅ Best for: Complex tasks requiring specialized expertise. 🔄 3. The Divide & Conquer (Parallel Pattern) Multiple AI agents work independently and sync up only when needed. 🔹 Example: Cybersecurity threat detection One agent monitors traffic Another detects breaches A third assesses severity A fourth deploys countermeasures ✅ Best for: Real-time or large-scale operations where speed matters. 🧠 4. The Ultimate Hybrid (Hybrid Pattern) Why choose one when you can mix them all? 🔹 Example: Autonomous driving A hierarchical AI oversees sub-agents for path planning, speed control, and obstacle detection Sequential decision-making (perception → action) Parallel execution for real-time traffic adaptation ✅ Best for: Dynamic, real-world environments that require flexibility. 💡 Final Thought: AI agents are evolving rapidly, and these design patterns are essential for making them more autonomous, adaptable, and scalable. Which pattern do you think will be the most useful in real-world AI applications? Drop a comment below! ⬇️ --- 👋 Hi, I’m Shyvee! I share insights on AI and the future of work. Subscribe for exclusive AI insights, programs, and a special invite to our AI Enthusiast Community powered by Microsoft Teams: https://lnkd.in/eR2ebrEM #AI #FutureofWork #ProductManagement