Developing AI Agents

The AI Agent Evolution: A Reality Check Looking at the rapid advancement of AI agents, I'm struck by the practical implications that aren't making headlines. Here's my take on where we really stand: 𝗧𝗵𝗲 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻: We've moved from basic text models to multi-modal systems that can process and generate across formats. But the real shift isn't just technical - it's functional. AI is transitioning from answering questions to executing complex workflows with minimal supervision. 𝗧𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗚𝗮𝗽: Most organizations are implementing AI capabilities piecemeal without redesigning workflows. The companies seeing transformative results are those treating AI agents as new team members rather than just tools. 𝗪𝗵𝗮𝘁'𝘀 𝗕𝗲𝗶𝗻𝗴 𝗢𝘃𝗲𝗿𝗹𝗼𝗼𝗸𝗲𝗱: The most significant leap isn't processing power - it's the integration of memory systems. Short and long-term memory capabilities mean interactions build upon each other rather than starting fresh each time. This fundamentally changes the relationship between humans and AI systems. 𝗧𝗵𝗲 𝗖𝗼𝗺𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: As autonomous decision-making capabilities expand, our governance frameworks aren't keeping pace. Who's responsible when an AI makes thousands of daily decisions? How do we maintain oversight without creating new bottlenecks? 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Instead of asking what AI can do, we should be asking how we redesign our organizations to leverage these capabilities effectively. What's your experience? Are you seeing AI agents transform workflows in your industry, or are we still in the experimentation phase?

I bet my entire career on one crazy prediction: AI Agents will transform enterprise operations more than cloud computing did. Today, IBM, NVIDIA, and PwC are our partners. Here's how I spotted what others missed: I was Director of AI Engineering at Clearbit, leading their enterprise AI products through acquisition. But I walked away from it all - including a massive retention bonus. Why? The signs were impossible to ignore. Large language models were reaching unprecedented capabilities, while computing costs plummeted. Traditional automation was failing enterprises spectacularly. Their systems were rigid, brittle, and couldn't adapt to change. That's when it hit me: AI Agents could bridge the gap between basic automation and true intelligence. They understand context, make decisions, and adapt on the fly. But the real opportunity? Enterprises would soon need thousands of these AI Agents. And they'd need a way to orchestrate them all. That's why we built CrewAI - to help companies deploy and manage AI Agents at scale. The response has been mind-blowing: • 50M+ agents executed in January alone • 90,000+ waitlist signups • Major partnerships with tech giants Here's what I learned about spotting massive opportunities: 1. Look for multiple trends converging • Advanced AI capabilities • Falling computing costs • Enterprise automation needs • API accessibility 2. Find markets desperate for transformation • Current solutions failing • Clear pain points • Massive potential impact 3. Timing is everything • Too early = market not ready • Too late = missed opportunity • Perfect timing = exponential growth The next wave of billion-dollar enterprises won't just use AI. They'll be built on autonomous AI Agents that think, decide, and act. If you're a decision-maker, you have two choices: 1. Watch others pioneer AI Agent adoption 2. Lead the charge and gain massive competitive advantage The cost of waiting? Potentially billions. Follow me for insights on: • AI Agent implementation • Enterprise automation • Future of work • Real-world case studies The future belongs to those who see it coming. And something massive is happening right now. Want to stay ahead? Follow me more on AI agents and enterprise tech.

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! 🤖

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

The narrative that “AI agents will replace software” makes for a good headline, but it misses what’s really happening. As this CIO Online article highlights, we’re not seeing the end of SaaS or traditional systems. We’re seeing a reimagining of how software works. AI agents are starting to reshape workflows, user experiences, and how work gets done. In my conversation with Clint Boulton, I emphasized that this shift is a game-changer, but only when grounded in reality. We’re using AI at J&J to rethink workflows and reduce friction, from software development to service operations. But we’re doing it with humans firmly in the loop. The idea of fully autonomous environments with thousands of agents operating unchecked isn’t practical, especially in a regulated industry like healthcare.  The future of software means more intelligent systems. That also means more complexity. The real leadership challenge is managing that complexity: building the right guardrails, designing for interoperability, and ensuring these technologies deliver measurable value. AI agents won’t replace enterprise systems – but they will change how we interact with them, and that’s where the real transformation begins.

Anthropic is killing it with these technical posts. If you're an AI dev, stop what you are doing and go read this. It shows, in great detail, how to implement an effective multi-agent research system. Pay attention to these key parts: Anthropic shares how they built Claude's new multi-agent Research feature, an architecture where a lead Claude agent spawns and coordinates subagents to explore complex queries in parallel. They use the orchestrator-worker architecture. This system allows Claude to dynamically plan, search, and synthesize high-quality answers across large corpora using web, workspace, and custom tool integrations. Orchestrator-Worker Design The lead agent decomposes a query, spins up specialized subagents (each with their own tools, prompts, and memory), and integrates their results. This parallel, breadth-first design dramatically improves performance for research tasks over sequential LLM use. It yields 90% higher success rates in internal evals compared to single-agent Claude. Token-efficient Scaling Performance gains correlate strongly with token usage and parallel tool calls. By distributing work across multiple agents and context windows, Claude’s system scales reasoning capacity efficiently. However, this comes with a 15× token cost over standard chats, making it suitable for high-value queries only. Prompt engineering is not dead! Anthropic iteratively refined agent behavior via prompt design. They embedded heuristics for task complexity scaling, delegation clarity, tool selection, and thinking strategies. They also used Claude to self-optimize prompt and tool use, reducing task times by 40%. Flexible Evaluation + Production Reliability Anthropic uses LLM-as-judge scoring with rubrics for factuality, citation, and efficiency, alongside human testing to catch subtle failures. For reliability, they built resumable stateful agents with checkpointing, rainbow deployments, and full observability of agent decision traces, crucial for debugging non-deterministic, long-running agents.

OpenAI's agent pricing isn't about AI at all. It's about the future of work. $2,000/month for knowledge workers $10,000/month for developers $20,000/month for PhD-level researchers The $20,000/month agent isn't the story. It's what happens next. It's the beginning of an economic reorganization we haven't seen since the Industrial Revolution. Here's what's really happening: → Traditional knowledge hierarchies are collapsing → The professional services model is being challenged → Career development pathways are vanishing → Size advantage is reversing completely We have seen this movie before: 1995: Internet eliminated information gatekeepers 2000: Enterprise software changed workflows 2011: Cloud democratized technology infrastructure This time is different. We're not just automating tasks – we're eliminating entire knowledge categories. Knowledge hierarchies were built because information had to flow up and decisions had to flow down. That entire paradigm is now shattering: → Middle management (20% of workforce) hollows out → A manager has 50+ agents instead of 7-10 humans → Companies maintain output with 70% smaller teams The impact will hit professional services first and hardest. Every consulting firm, law practice, and advisory business is built on the same foundations: time-based billing, junior staff leverage, and utilization rates. Agents obliterate each assumption: → Production time collapses by 90% → Junior roles vanish when agents handle analysis → Utilization metrics become meaningless when work scales infinitely The math is simple: A $240K/year PhD-level agent costs the same as 2-3 human PhDs but works 24/7 with no benefits, vacation, or turnover. It can handle 5-10x the workload of a single researcher. MBB, Big 4, and AmLaw 100 firms will see their entire model challenged as power dynamics are completely inverting. For decades, scale meant competitive advantage. Not anymore. The winners won't be the biggest firms. They'll be the fastest to rebuild around agent augmentation. This transformation creates three imperatives: → Organizations must adapt their structures now → Teams need to reimagine how work gets distributed → Leaders must reconsider where human value truly lies The long-term shift isn't just a technology change – it's a fundamental rewiring of economic value creation. Those who recognize this early will thrive; those who wait will find themselves playing catch-up in an entirely new landscape. The real divide isn't between humans and machines. It's between those who recognize this shift early and those who deny it until it's too late. How is your business adapting to the changing landscape?

💬 From buzzword to boardroom: AI Agents have entered the conversation. Mentions of AI Agents in earnings calls have surged 331% in the past year, signaling a major shift in how businesses think about automation and intelligence. And for good reason—unlike traditional GenAI, AI Agents don’t just assist; they observe, plan, and act autonomously, transforming entire workflows. The impact? These business cases speak for themselves: ➡ Marketing: AI agents cut content costs by 95% and sped up production 50x—turning a 4-week process into a single day at a leading consumer packaged goods company. ➡ Customer Service: A global bank reduced service costs by 10x with AI-powered agents. ➡ Research and Development: At a biopharma company, AI agents cut lead generation time by 25% and boosted efficiency 35% in clinical study reports. With the AI agent market projected to grow 45% annually, surpassing $50 billion by 2030, businesses that successfully embed AI agents into their core processes will unlock productivity, personalization, and entirely new business models. 📰 Explore BCG’s latest insights on Agentic AI to learn more: https://lnkd.in/e2nGhjS2 How is your organization preparing for this shift? #ArtificialIntelligence #Technology #Innovation #Business #BCG

𝗪𝗵𝘆 𝟰𝟬% 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮𝗯𝗮𝗻𝗱𝗼𝗻𝗲𝗱 𝗯𝘆 𝟮𝟬𝟮𝟳? It’s not the agents. It’s not the tools. It’s the architecture. Agentic AI is the next frontier, systems where multiple autonomous agents plan, reason, and communicate to solve complex tasks. But many teams build agent demos in notebooks, then hit a brick wall trying to productionize. The real problem? Most agentic AI efforts start as fragile experiments without a solid engineering backbone. What goes wrong? 1️⃣ Protocol Chaos When agent-to-agent messages aren’t standardized, everything breaks. Successful teams use MCP (Model Context Protocol) and clean registries from day one. 2️⃣ Tool Fragmentation Hard-coding tools inside agents might work for a demo, but modular tool interfaces are critical for scale and future maintenance. 3️⃣ Missing Coordination Layer Multiple agents with no shared planner? That’s a recipe for confusion. A well-defined coordinator module is essential. 4️⃣ No Communication Bus Agent communication without a message bus quickly turns into spaghetti code. The solution? Architect for production on day one: - Clear separation of config - Modular tool orchestration - Robust communication protocols - Reasoning and planning layers Building agentic systems isn’t just prompt engineering. It’s designing a multi-agent architecture that can actually survive the real world. #AgenticAI #AIengineering #MCP #GenerativeAI