Developing AI Agents

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! 🤖

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

AI Agent vs Agentic AI Most people use the terms AI Agent and Agentic AI like they mean the same thing. They don’t. The difference isn’t just semantic. It’s architectural. Here’s how the tech stack evolves from AI Agent → Agentic AI 👇 1. Intelligence models - AI Agent typically relies on a single LLM with prompt → response workflows. - Agentic AI moves toward multi-model reasoning, planner–executor setups, and hybrid inference across systems. 2. Architecture & frameworks - AI Agent often follows a single-agent, linear execution flow. - Agentic AI introduces multi-agent systems, goal-driven workflows, and orchestration frameworks like LangGraph, CrewAI, or AutoGen. 3. Memory systems - AI Agent works with session memory, short-term embeddings, and basic caches. - Agentic AI adds long-term memory layers, episodic + semantic memory, knowledge graphs, and vector databases. 4. Tool usage & actions - AI Agent uses predefined tools and function calling triggered by users. - Agentic AI autonomously selects tools, plans multi-step executions, interacts with environments, and uses structured tool registries. 5. Knowledge & retrieval - AI Agent typically uses basic RAG pipelines with static retrieval. - Agentic AI evolves into adaptive RAG, context prioritization, hybrid search, and continuously updated knowledge graphs. 6. Orchestration & workflows - AI Agent runs sequential flows and simple backend automation. - Agentic AI uses orchestration engines, planning loops, event-driven workflows, and reflection cycles. 7. Decision making - AI Agent is reactive and prompt-driven. - Agentic AI is goal-oriented, with planning, self-evaluation, and iterative reasoning loops. 8. Deployment - AI Agent is often deployed as chatbots, copilots, or API-based assistants. - Agentic AI becomes autonomous platforms, digital workforce agents, and persistent execution systems. 9. Monitoring & observability - Both need logs, monitoring, and error tracking but Agentic AI requires deeper analytics, response monitoring, and system-level feedback loops. 10. Learning & improvement - AI Agent improves through prompt iteration and occasional fine-tuning. - Agentic AI evolves through continuous feedback pipelines, performance adaptation, and evaluation frameworks. AI Agent = intelligent responder. Agentic AI = autonomous system with goals, memory, tools, and orchestration. One answers questions. The other executes objectives. Are you building smarter responses or autonomous systems?

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!

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?

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

In the next phase, AI agents will be autonomous economic participants. The economy will evolve dramatically as agents operate continuously, share perfect information, and rapidly adapt. However our existing human-centric economy is not designed for agents. A very interesting paper “Unlocking AI Agents Potential Through Market Forces” (link in comments) explores in detail the barriers to the economic potential, and the enablers to move past those. 🚧 Human-centric infrastructure as a barrier. The current digital ecosystem was built for human users, with interfaces, identity verification, and payment systems designed around human behavior. These constraints prevent AI agents from seamlessly integrating into digital economies, limiting their ability to create and exchange value autonomously. 🔍 Challenges in service discovery. AI agents struggle to find and evaluate services because discovery mechanisms—such as industry events, peer recommendations, and human-oriented documentation—are not machine-readable. Future solutions must include structured registries, machine-friendly descriptions, and automated indexing for real-time service discovery. 🔑 Identity and authorization limitations. AI agents lack traditional identity markers like physical documents, email addresses, and human-verifiable credentials. Current authentication methods are slow and require human intervention, making them unsuitable for machine-speed operations. Cryptographic identity systems, decentralized reputation models, and dynamic access control could solve these challenges. 🌐 Software interfaces designed for humans. Digital services currently separate human-friendly visual interfaces from APIs meant for machine interactions, creating inefficiencies for AI agents. Future systems should support adaptive, machine-readable interfaces that dynamically adjust based on the consumer, whether human or AI. 💰 Payment systems block AI participation. Online transactions rely on human verification, anti-bot measures, and rigid business models like subscriptions and credit card payments. AI-friendly payment solutions should incorporate cryptographic attestation, machine-scale wallets, and real-time micropayments to enable seamless economic activity. 🚀 Future infrastructure for AI-driven markets. To fully integrate AI agents into digital markets, the ecosystem needs machine-readable service discovery, scalable identity and authorization systems, flexible payment mechanisms, and new market protocols. These advancements will unlock economic efficiency, innovation, and autonomous value creation at an unprecedented scale. This is a central theme in my work on AI-driven business model innovation, I will be sharing a lot more related insights on this.

3 years ago today, ChatGPT launched. Today, it’s quietly becoming a FinTech superpower and the new backbone of global commerce 😳 This week, OpenAI quietly launched Shopping Research inside ChatGPT. It looks small on the surface, but in reality, this is the moment the internet’s entire commercial model starts to flip. For 25 years, online shopping followed the same pattern: Search → Compare → Click → Buy. AI now collapses the funnel into a single conversation. → You tell it what you need. → It scours the whole web. → It filters prices, reviews, and availability. → It explains trade-offs. → And soon - it buys for you. No ads. No SEO. No sponsored placement. Just an AI agent making decisions on your behalf 🤖 This is OpenAI’s real strategy becoming clear & visible: Browser → Payments → Finance → Agents - all converging into one ecosystem. Of course, they're not alone. ↳ Google is rolling out conversational shopping built on its 50-billion-item Shopping Graph, even letting its AI call real stores for inventory checks. ↳ Perplexity AI partnered with PayPal to let you purchase directly inside chat. Because everyone sees where this is heading: The interface for commerce is shifting from webpages to AI agents 🤖 And when that happens, the economics flip instantly: - High-intent queries drain out of search engines. - Ads lose their leverage. - Pricing wars break. - Payment networks rewire themselves. And the battle becomes: Who owns the agent that makes the decisions? Visa, Mastercard, PayPal, Stripe, and OpenAI are already racing to build tokenized, programmable rails for agent-to-agent transactions, a market projected to grow from $7 billion to $93 billion by 2032. But the real takeaway is this: This isn’t about shopping. It’s about the architecture of money. The same rails that let you buy shoes through an AI agent will soon orchestrate autonomous B2B payments, real-time credit checks, contract negotiations, and machine-to-machine commerce. If your product, service, or API isn’t legible to agents, you simply won’t appear in the new economy. So, 3 years after ChatGPT launched, the story is no longer about AI being “smart”. It’s about AI becoming the buyer, the researcher, the negotiator, and soon - the customer.

A new MIT and Harvard study just explained why AI agents could rewrite the structure of business itself. Not by automating tasks, but by collapsing coordination. The paper, “Demand, Supply, and Market Design with AI Agents”, introduces what it calls the Coasean Singularity. It describes a world where the cost of coordinating work, trade, and trust falls close to zero. Ronald Coase’s 1937 insight was simple but profound: Firms exist because markets are expensive to use. Every department, meeting, and process we build is a workaround for human friction like search, negotiation, and enforcement. Now AI agents are starting to handle those frictions directly. → Matching suppliers faster than procurement teams → Negotiating logistics in seconds → Soon, completing contracts machine-to-machine, with humans only reviewing exceptions When coordination costs fall, the shape of the firm begins to change. We start to move from hierarchies to networks, from organisations to orchestrations. The economy begins to behave more like software than structure. And when coordination becomes almost free, something new happens. Entirely new markets appear. Agents make it viable to trade micro-services, on-demand data, or one-off insights that were once too small or complex to coordinate. The long tail of the economy comes alive. The paper also argues that efficiency creates a new constraint: alignment. When every buyer and seller has an agent acting on their behalf, markets no longer run on trust. They run on how well those agents understand what we truly value. Market design becomes a question of alignment, not just speed. At the same time, the largest platforms are already tightening control of their ecosystems. Meta plans to block external AI models from WhatsApp Business. Amazon is restricting autonomous crawlers and agents across its retail platform. These are not small policy changes. They are early signs of a power struggle over who governs the agentic economy. If agents become the main interface between humans and commerce, those who control the gateways will decide how value flows. Forward-looking companies are preparing for this. They are rethinking how coordination itself happens, building open agent ecosystems, shared standards, and transparent protocols before the walls close in. Beyond economics lies another challenge: identity. As agents transact on our behalf, the real bottleneck will be trust. Which agents represent real people? Which are synthetic? Proof-of-personhood and verification will become the foundation of digital markets. AI will not replace capitalism overnight. But it is already rewiring the plumbing of coordination, one process, one decision, and one transaction at a time. P.S. If your 2025 plan still treats AI as a feature rather than infrastructure, you are building for the wrong outcome. How will your business operate when every process can think, bargain, and act, yet still needs human judgement to guide it?