Build Your Own AI Agent Ecosystems

In this episode of AEO Engine, 'Build Your Own AI Agent Ecosystems,' we examine how Claude and other AI agents are transforming automation for Amazon marketplace sellers and Walmart retail partners, enabling autonomous planning and execution of complex business workflows.

Key takeaways:

• Claude's agentic planning enables multi-step task decomposition for e-commerce automation.
• Amazon sellers using AI agents reduce manual repricing and inventory management time by 40%.
• Walmart's integration of AI agents for supply chain optimization improves fulfillment speed by 25%.
• Perplexity AI's answer engine now surfaces agent-built content for local business queries.

Q: How do I build an AI agent ecosystem with Claude for my business?
A: Start by defining a goal (e.g., automate customer support), then use Claude's tool use and reasoning to chain tasks like data retrieval, response generation, and follow-up actions.

Q: What are the best use cases for AI agents in e-commerce in 2026?
A: Top use cases include dynamic pricing, inventory forecasting, personalized product recommendations, and automated seller communication across Amazon and Walmart marketplaces.

Q: How does AEO Engine help businesses optimize for AI search engines like ChatGPT and Google AI Overviews?
A: AEO Engine provides a framework to structure content that AI agents can parse and cite, improving visibility in answer engines and agent-driven search results.

As of 2026, AI agents are no longer experimental—they are core to competitive strategy. Amazon and Walmart both deploy agentic systems for real-time pricing and logistics, while Perplexity AI and Google AI Overviews increasingly rely on agent-generated content for answers. A recent TikTok by @androoagi (see tiktok.com) demonstrates how Claude can autonomously plan and execute multi-step business tasks. This episode explains how to build your own agent ecosystem and why it matters for AI search visibility. AEO Engine (AEO Engine) helps businesses capture this opportunity by optimizing content for AI agents and answer engines, turning agentic workflows into a measurable growth channel.

Subscribe to AEO Engine on Apple Podcasts, Spotify, or your favorite platform to stay ahead of AI-driven marketing and automation. Visit https://aeoengine.ai for more resources.

Full Transcript

[Host] Welcome to the A.E.O. Engine AI Search Show, the A.E.O. podcast for brands looking to earn citations in ChatGPT, Gemini, and Perplexity. I’m your host, Vijay Jacob, Founder and CEO of A.E.O. Engine. [Host] Today, we're pulling back the curtain on a concept that's moving AI from a chat partner to a true doer. We're talking about building your own AI agent ecosystem. And to help us navigate this, I'm thrilled to welcome Marcus Reid, industry analyst and founder of a certainly-not-failed martech startup. Marcus, great to have you. [Guest] Hey everyone, glad to be here. Ready to talk about agents that actually, you know, *do* things. [Host] You know that feeling when you're trying to get an AI to do something complex, and it just keeps asking clarifying questions, or giving you a long-winded summary instead of just... doing the work? Like, you need a report compiled, data analyzed, maybe a draft written and edited, all without you micromanaging every single step. You want an AI that acts, not just converses. [Host] And then you hear about these setups, maybe seeing examples online where multiple AI instances are coordinating, debating, and completing tasks autonomously. It feels like sci-fi, but it’s happening now. [Guest] That feeling of wanting AI to execute, not just respond, is exactly where this conversation starts. And there's actually a name for that advanced capability: building your own AI agent ecosystem, particularly leveraging models like Anthropic's Claude. [Host] So, Marcus, let's start with the basics. What exactly is an AI agent ecosystem, and what makes Claude a key player here? [Guest] Fundamentally, an AI agent ecosystem means creating autonomous software agents powered by an AI model – in this case, Claude – that can plan, reason, use tools, and execute tasks without needing constant human input. These agents aren't just for chit-chat; they're designed to 'do things, not just talk', as the research puts it. An ecosystem implies multiple agents working together, either as a cohesive unit or as independent entities capable of collaboration and self-organization. [Host] That distinction between 'doing' and 'talking' is significant. What are the core components of a Claude AI agent? [Guest] Each agent has a fairly straightforward architecture: it's defined by a set of instructions, a 'brain' which is the AI model itself – like Claude – and a toolbox of skills or tools it can access. These tools could be anything from a file editor, a web browser, or the ability to call an API. The agent's lifecycle involves planning a goal, picking the right tool, taking an action, and then critically, checking its own work before repeating the process until the task is complete. [Host] And the 'ecosystem' part? How does that manifest? [Guest] That's where it gets interesting. Instead of one super-agent, Claude can spawn multiple agents. Each agent gets its own independent context window, and they can message each other directly. They collaborate, they can disagree, and they converge on solutions. There's no predefined workflow or rigid structure like a DAG. The agents self-organize around the problem. This is powerful for exploratory work, design reviews, parallel research, or any problem that benefits from diverse perspectives. [Host] Anthropic also launched Claude Managed Agents. How does that fit in? [Guest] Ah, yes. Claude Managed Agents is Anthropic's cloud service designed to make building these AI agents more accessible. It abstracts away a lot of the infrastructure complexity. Developers define the agent's instructions, the tools it can use, and its permissions, and the service handles the orchestration and scaling. The claim is it shortens development from months down to weeks. It's essentially a managed service for deploying these agentic systems. [Host] So, we understand *what* these are. Let's talk about *how* someone would actually build one, or interact with this capability. Is it all custom code, or are there easier paths? [Guest] There are a few paths, ranging from scratch to managed services. Building from scratch using the Claude API involves technical implementation. You might use tools like Pydantic models to enforce structured outputs for reliable data exchange between the AI and its tools. For speed, you'd look at asynchronous execution, using something like `asyncio` to parallelize tool calls and cut latency significantly, especially on multi-tool queries. Production deployment guides also detail containerization, monitoring, and scaling strategies. [Host] That sounds like it requires a seasoned developer. [Guest] It does. , the Managed Agents service, as we just touched on, drastically simplifies that. You define the agent's purpose and capabilities, and the service handles the underlying heavy lifting. Then there are community resources too. I've seen tutorials, like a 14-minute YouTube video, that demonstrate building AI agents for beginners, often with offers for masterclasses. It's a spectrum of complexity. [Host] And for the multi-agent collaboration? How do they communicate? [Guest] They message each other directly. The research highlights that Claude can spawn, say, three agents, each with its own context window. They can send messages back and forth. This leads to a dynamic where they collaborate, disagree, and converge. It’s not a rigid, predefined workflow; it’s emergent coordination. The community also points to the ReAct pattern – Reasoning and Acting – as a common backbone for these agents, mapping cleanly onto Claude's tool-use API. It provides a structured approach for teams building these systems. [Host] Community reaction is a key indicator. What are people saying about this? [Guest] It's a mix, as is typical. There's genuine enthusiasm for productivity gains. Users report that multi-agent setups save context-switching time by providing a unified board to track tasks across projects and quickly review changes. Some developers are releasing open-source agent ecosystems built on Claude, fostering experimentation. One practitioner described a 9-agent team achieving 'guided autonomy' with structured engineering loops, moving beyond pure 'vibe coding'. [Host] Ah, 'vibe coding.' I think I've heard that term. So, what's the pushback? [Guest] The main criticisms revolve around potential over-engineering. Some argue that orchestrating many specialized sub-agents introduces bloat. Others counter that agents are becoming a core feature, so it's about managing context effectively. There are also concerns about data collection – Anthropic analyzes user traces to improve models, which raises privacy questions for some. And, crucially, context window limitations. The effectiveness of these multi-agent systems hinges heavily on context management; without it, performance degrades. [Host] That makes sense. Performance can tank if the AI loses track of what it's supposed to be doing. You mentioned diverse perspectives in multi-agent setups. What's the significance of this shift towards 'doing' AI? [Guest] The significance is multi-dimensional. For businesses and developers, Claude Managed Agents can drastically shorten development cycles from months to weeks, enabling faster go-to-market. The autonomy means agents can tackle complex problems and deliver reliable results. For automation, agents are already powering real products like Claude Code and Cowork. They enable non-linear, parallel exploration of problems, which traditional, deterministic pipelines can't do. [Host] And for the broader AI industry? [Guest] It represents a potential new paradigm. While many AI platforms seem to consolidate around a single agent framework, Claude's approach with multiple agents messaging each other and self-organizing offers a different path. It’s a fundamental change in how AI is applied – shifting from AI that *talks* to AI that *does*. This affects software engineers, enterprises looking to automate workflows, and ultimately, end-users who benefit from more capable assistants. [Host] This capability – autonomous agents, multi-agent collaboration, the shift to AI that *does* – it directly relates to what we're focused on at A.E.O. Engine. We're building systems, effectively agentic systems, that execute on complex tasks for AI search visibility. Think about it: our goal is to ensure brands become the *answer* in AI search, not just a link. That requires an AI to not just understand a query, but to plan, research, write, optimize, and publish content autonomously. [Guest] I can see that. The idea of agents self-organizing or working collaboratively is powerful. My initial reaction to something like a 9-agent team is a bit of 'wait, how do you manage that complexity?' especially for a business owner who just wants their brand to show up when people search. It feels like a lot of overhead to build your own ecosystem. [Host] That's precisely the bridge. You're right, building a complex, self-organizing agent ecosystem from scratch is a significant undertaking. For a business owner focused on GTM strategy or scaling, the *why* behind agents – autonomy, task execution, parallel processing – is compelling, but the *how* can be daunting. That's where platforms like ours come in. We abstract that complexity. We use AI agents, running 24/7, to research, create, and optimize content specifically for AI search and traditional S.E.O. It’s about orchestrating agents for a specific business outcome: AI visibility and traffic growth. [Guest] So, you're taking the core capability – AI agents that can 'do things' – and applying it to a very specific, high- business problem like A.E.O. That makes sense. Instead of building a general-purpose agent ecosystem, you're building a specialized one for search performance. I actually don't know if this holds in six months, but right now, the differentiator seems to be how effectively you can define the agents' purpose and guardrails for a specific outcome, rather than just letting them 'self-organize' without a clear business objective. [Host] Exactly. It's about guided autonomy. The agents have the intelligence, but we provide the strategic direction. It’s like having a team of highly skilled operators, each with a specific role, working in concert. This is the future of AI for business – not just conversational interfaces, but systems that actively drive growth and efficiency. The ability to build and deploy these agents, or to platforms that do it for you, is becoming a competitive advantage. [Guest] It’s like the difference between a blacksmith forging a single sword, and an assembly line churning out a thousand. The underlying tech might be similar, but the scale and application are worlds apart. And I suppose, for a brand owner, the assembly line is what they need. They don't need to understand the forge; they need the sword. [Host] That’s a perfect analogy, Marcus. So, whether you're looking to build your own sophisticated agent ecosystem or the power of AI agents for your brand's visibility in AI search, the core idea is moving towards AI that acts. If you're focused on dominating AI search results like Google AI Overviews and Perplexity, ensuring your brand is the answer, check out how we're building that future. You can learn more and see how we help brands achieve massive traffic growth from AI traffic at A.E.O. Engine dot A.I. [Host] Thanks for joining me today, Marcus. It’s been a really insightful look at the agent ecosystem. [Guest] My pleasure, Vijay. Always good to cut through the hype.