Agentic AI Skills and Subagent Design

As we move from LLM-powered chatbots to truly 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, understanding 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 becomes non-negotiable. Agentic AI isn’t just about plugging an LLM into a prompt—it’s about designing systems that can 𝗽𝗲𝗿𝗰𝗲𝗶𝘃𝗲, 𝗽𝗹𝗮𝗻, 𝗮𝗰𝘁, 𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻 in dynamic environments. Here’s where most teams struggle:  They underestimate the 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 required to support agent behavior. To build effective AI agents, you need to think across four critical dimensions: 1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Agents should break down goals into executable steps and act without constant human input. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Agents need long-term and episodic memory. Vector databases, context windows, and frameworks like Redis/Postgres are foundational. 3. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – Real-world agents must invoke APIs, search tools, code execution engines, and more to complete complex tasks. 4. 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 – Single-agent systems are powerful, but multi-agent orchestration (planner-executor models, role-based agents) is where scalability emerges. The ecosystem is evolving fast—with frameworks like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜 making it easier to move from prototypes to production. But tools are only part of the story. If you don’t understand concepts like 𝘁𝗮𝘀𝗸 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹𝗻𝗲𝘀𝘀, 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻, and 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀, your agents will remain shallow, brittle, and unscalable. The future belongs to those who can 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗟𝗟𝗠 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗿𝗼𝗯𝘂𝘀𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻. That’s where real innovation happens. 2025 will be the year we go from prompting to architecting.

“Building AI agents” This is the new trend But very few know what it actually takes to run them in production. Being an Agentic AI Engineer isn’t just about calling an LLM and adding tools. It’s about designing systems that can reason, act, recover from failure, and improve over time. This cheat sheet breaks the role into the real building blocks: You start with Python - async workflows, APIs, data pipelines, and clean project structure. This is the foundation for everything agents do. Then come APIs and integrations, where agents connect to real systems using authentication, retries, rate limits, and agent-friendly endpoints. RAG and vector databases give agents memory beyond context windows - handling ingestion, embeddings, semantic search, re-ranking, metadata filtering, and knowledge refresh. Security matters early: sandboxing, permissions, secrets management, prompt-injection defense, and audit logs are non-negotiable once agents touch real data. Observability tells you what your agents are actually doing in production - traces, logs, latency, token usage, errors, and behavioral drift. LLMOps keeps everything running at scale: prompt versioning, model routing, fallbacks, cost optimization, and continuous improvement. System design turns prototypes into platforms: queues, background workers, stateless vs stateful agents, failure handling, and horizontal scaling. Cloud makes it real: containers, environments, secrets, monitoring, and cost-aware deployments. Agent frameworks structure reasoning itself — planning loops, task decomposition, tool calling, multi-agent coordination, memory, and reflection. Evaluation closes the loop: task success metrics, hallucination detection, tool accuracy, and human feedback. And finally, product thinking ties it all together - solving real user problems, defining agent responsibilities, keeping humans in the loop, and iterating toward outcomes. The takeaway: Agentic AI is not a single tool or framework. It’s a full-stack discipline spanning engineering, infrastructure, operations, safety, and product. If you want to build agents that actually work in the real world - this is the roadmap.

Agentic AI Design Patterns are emerging as the backbone of real-world, production-grade AI systems, and this is gold from Andrew Ng Most current LLM applications are linear: prompt → output. But real-world autonomy demands more. It requires agents that can reflect, adapt, plan, and collaborate, over extended tasks and in dynamic environments. That’s where the RTPM framework comes in. It's a design blueprint for building scalable agentic systems: ➡️ Reflection ➡️ Tool-Use ➡️ Planning ➡️ Multi-Agent Collaboration Let’s unpack each one from a systems engineering perspective: 🔁 1. Reflection This is the agent’s ability to perform self-evaluation after each action. It's not just post-hoc logging—it's part of the control loop. Agents ask: → Was the subtask successful? → Did the tool/API return the expected structure or value? → Is the plan still valid given current memory state? Techniques include: → Internal scoring functions → Critic models trained on trajectory outcomes → Reasoning chains that validate step outputs Without reflection, agents remain brittle, but with it, they become self-correcting systems. 🛠 2. Tool-Use LLMs alone can’t interface with the world. Tool-use enables agents to execute code, perform retrieval, query databases, call APIs, and trigger external workflows. Tool-use design involves: → Function calling or JSON schema execution (OpenAI, Fireworks AI, LangChain, etc.) → Grounding outputs into structured results (e.g., SQL, Python, REST) → Chaining results into subsequent reasoning steps This is how you move from "text generators" to capability-driven agents. 📊 3. Planning Planning is the core of long-horizon task execution. Agents must: → Decompose high-level goals into atomic steps → Sequence tasks based on constraints and dependencies → Update plans reactively when intermediate states deviate Design patterns here include: → Chain-of-thought with memory rehydration → Execution DAGs or LangGraph flows → Priority queues and re-entrant agents Planning separates short-term LLM chains from persistent agentic workflows. 🤖 4. Multi-Agent Collaboration As task complexity grows, specialization becomes essential. Multi-agent systems allow modularity, separation of concerns, and distributed execution. This involves: → Specialized agents: planner, retriever, executor, validator → Communication protocols: Model Context Protocol (MCP), A2A messaging → Shared context: via centralized memory, vector DBs, or message buses This mirrors multi-threaded systems in software—except now the "threads" are intelligent and autonomous. Agentic Design ≠ monolithic LLM chains. It’s about constructing layered systems with runtime feedback, external execution, memory-aware planning, and collaborative autonomy. Here is a deep-dive blog is you would like to learn more: https://lnkd.in/dKhi_n7M

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!

Agent Skills just crossed 31,000 stars on GitHub! 🌟 I'm incredibly grateful to see this approach resonating with so many of you. Here's my new write-up on engineering ideas I use in the skills: https://lnkd.in/gF5JjA67 This is the reality of agentic coding right now: A senior engineer's job is mostly the parts that don't show up in the diff. Specs. Tests. Reviews. Scope discipline. Refusing to ship what can't be verified. By default, AI coding agents skip all of those parts. They take the shortest path to "done." Ask for a feature, and it writes the feature. It doesn't ask for a spec or write a failing test first. It produces code, declares victory, and moves on - the exact same failure mode every senior engineer has spent their career learning to avoid. Agent Skills is my attempt to bolt that senior-engineer scaffolding back onto the AI. It's a system to make those invisible, critical steps non-negotiable. I wrote a new post detailing the design choices behind the project, how it maps to Google's published engineering practices, and what you can steal from it for your own teams (even if you never install a single skill). A few core principles: 1. Process over prose: Workflows are agent-actionable 2. Anti-rationalization tables: Pre-written rebuttals to the lies agents (and tired engineers) tell themselves to skip best practices ("I'll write tests later"). 3. Verification is non-negotiable: Every task must terminate in concrete evidence. "Seems right" is never enough. 4. Scope discipline: The single biggest determinant of whether an agent's PR is mergeable. Touch only what you're asked to touch. AI agents are extremely capable junior engineers with no instinct for the invisible parts of the job. The senior-engineer parts are no longer optional, even when the engineer is a model. #ai #programming #softwareengineering

🌟 New Paper: AI Agents vs. Agentic AI Interesting paper summarizing distinctions between AI Agents and Agentic AI. It also talks about the key ideas, solutions, and the future. Here are my notes: ⚪ What is the paper about? The paper provides a comprehensive taxonomy and comparison between AI Agents and Agentic AI, clarifying their conceptual, architectural, and operational differences. ⚪ What are AI Agents? AI Agents are single-entity systems enhanced with LLMs and external tool integration, capable of task-specific autonomy and sequential reasoning. They are reactive, modular, and typically used for narrow applications like email triage, scheduling, or customer service. ⚪ What is Agentic AI? Agentic AI represents an architectural shift. These systems involve multiple collaborating agents with dynamic task decomposition, persistent memory, and orchestration layers. They enable higher-level coordination and are suited for complex workflows like research automation, robotic swarms, and medical diagnostics. ⚪ Application Mapping AI Agents: Email filtering, report summarization, content recommendation, customer support. Agentic AI: Coordinated research assistants, ICU decision support, robotic orchard harvesters, adaptive game AIs. ⚪ Challenges AI Agents: Limited causal reasoning, hallucinations, lack of proactivity, brittle long-horizon planning. Agentic AI: Inter-agent error cascades, emergent instability, opaque communication, scalability, explainability, and security vulnerabilities. ⚪ Key Architectural and Algorithmic Solutions - Retrieval-Augmented Generation (RAG) - Tool-augmented reasoning (function calling) - Agentic Loop: Reasoning, Action, Observation - Memory Architectures (Episodic, Semantic, Vector) - Multi-agent orchestration with Role Specialization - Reflexive and Self-Critique Mechanisms - Programmatic Prompt Engineering Pipelines - Causal Modeling and Simulation-based Planning - Monitoring, Auditing, and Explainability Pipelines - Governance-aware design with role isolation and traceability There are all important areas that researchers and developers need to get familiar with to build reliable and robust agentic systems. ⚪ Future Roadmap For AI Agents: Proactive intelligence, continuous learning, trust & safety. For Agentic AI: Multi-agent scaling, simulation-based planning, ethical governance, and domain-specific systems. These are all areas that need huge innovations in algorithms, architectures, infrastructure, protocols, and enhancing the models themselves.

I'm thrilled to share our latest Substack deep-dive: a guide to the emerging AG-UI and A2UI Agentic AI protocols that are bringing structure, security, and intelligence to agentic browser interactions! Co-authored with my friend Idan Habler, PhD In my previous article, I outlined a vision for next-gen Agentic Browsers with five critical components—from schema extraction to privacy sandboxes. More detail about this article is in comment field. Today, I'm excited to report: the industry is moving in exactly this direction. What's happening right now: CopilotKit's AG-UI (Agent-User Interaction) protocol and Google's A2UI (Agent-to-User Interface) spec are addressing two of the most critical challenges: Structured UI schemas that agents can actually understand (no more brittle pixel-scraping!) Bi-directional runtime channels for stateful, intelligent agent↔UI orchestration What we still need: 1: Standardized capability detection manifests 2: Hardened privacy sandboxes 3: First-class feedback/learning loops But make no mistake—this is a pivotal moment. We're witnessing the shift from fragile browser automation to robust, agentic ai native interaction approach. In this new article, you'll discover: ✅ How AG-UI and A2UI work together architecturally ✅ Practical implementation patterns and code examples ✅ Security analysis through the MAESTRO Threat Modeling Framework— because agentic systems must be secure by design The convergence is happening. The standards are emerging. And the future of human-agent collaboration is being built right now. 🔗 Link to article in comment field What's your take on these protocols? Are you building with agentic UIs? Let's discuss in the comments! 👇 #AI #AgenticAI #AIAgents #BrowserAutomation #Cybersecurity #ThreatModeling #Innovation #TechLeadership

The next evolution of AI agents is here! Is your LLM agent struggling with real-world organizational tasks? General knowledge isn't sufficient. For true utility, agents require procedural expertise and domain-specific context. Anthropic has recently unveiled Agent Skills, a transformative approach to specializing AI agents (like Claude) by providing them with composable, scalable 'onboarding guides'. Here’s why this is significant for the future of AI in the enterprise:- 1. Progressive Context:- Skills are organized folders of instructions and resources, utilizing a principle called Progressive Disclosure. The agent first loads high-level metadata, followed by full instructions, and finally any linked files or code, all on demand. This approach maintains a lean context window while ensuring knowledge is effectively unbounded. 2. Deterministic Reliability:- Skills extend beyond text. They can bundle pre-written code execution scripts (e.g., Python) for tasks requiring deterministic, reliable outcomes, such as sorting data or completing complex PDF forms. This advancement moves beyond mere token generation. 3. Scalability via Simplicity:- Consider a Skill as an 'onboarding guide' for a new hire. Developers and organizations can now easily codify and share proprietary workflows, transforming general-purpose models into highly specialized tools. The capability to dynamically equip and specialize agents with organizational knowledge and reliable code marks a significant advancement toward production-ready AI systems. To see it in action, check out the video demonstrating Skills Chaining, where Claude seamlessly switches between a PowerPoint Skill, a Brand Guidelines Skill, and a Design Skill in one conversation. Read the full engineering deep dive here:- https://lnkd.in/dmGcBDqW Watch the video here:- https://lnkd.in/d2VxMYrG #AIAgents #LLMs #Anthropic #AgentSkills #GenerativeAI #FutureofWork

Agentic AI is quietly reshaping UX research and human factors. These systems go beyond isolated tasks - they can reason, adapt, and make decisions, transforming how we collect data, interpret behavior, and design with real users in mind. Currently, most UX professionals experiment with chat-based AI tools. But few are learning to design, evaluate, and deploy actual agentic systems in research workflows. If you want to lead in this space, here’s a concise roadmap: Start with the core skills. Learn how LLMs work, structure prompts effectively, and apply Retrieval-Augmented Generation (RAG) to tie AI reasoning into your UX knowledge base: 1) Generative AI for Everyone (Andrew Ng) - broad introduction to generative AI, prompt engineering, and how generative tools feed autonomous agents. https://lnkd.in/eCSaJRW5 2) Preprocessing Unstructured Data for LLM Apps - shows how to structure data for AI-driven research. https://lnkd.in/e3AKw8ay 3)Introduction to RAG - explains retrieval-augmented generation, which makes AI agents more accurate, context-aware, and timely. https://lnkd.in/eeMSY3H2 Then you need to learn how agents remember past interactions, plan actions, use tools, and interact in adaptive UX workflows. 1) Fundamentals of AI Agents Using RAG and LangChain - teaches modular agent structures that can analyze documents and act on insights. This one has a free trial. https://lnkd.in/eu8bYdjh 2) Build Autonomous AI Agents from Scratch (Python) - hands-on guide for planning and prototyping AI research assistants. This one also has a free trial. https://lnkd.in/e8kF-Hm7 3) AI Agentic Design Patterns with AutoGen - reusable architectures for simulation, feedback analysis, and more. https://lnkd.in/eNgCHAss 3) LLMs as Operating Systems: Agent Memory - essential for longitudinal studies where memory of past behavior matters. https://lnkd.in/ejPiHGNe Finally, you need to learn how to evaluate, debug, and deploy agentic systems at scale in real-world research settings. 1) Building Intelligent Troubleshooting Agents - focuses on workflows where agents help researchers address complex research challenges. https://lnkd.in/eaCpHXEy 2) Building and Evaluating Advanced RAG Applications - crucial for high-stakes domains like healthcare, where performance and reliability matter most. https://lnkd.in/eetVDgyG

𝗧𝗟;𝗗𝗥: Multi-agent AI mirrors distributed systems, facing ACR tradeoffs (Agency, Control, Reliability). Like the CAP theorem, you can't optimize all three. Anthropic's research shows the winning enterprise patterns. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗜 = 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Everyone thinks agentic AI is magic (and hence the hype), but agentic AI is actually just distributed system architecture—orchestrating specialized agents across compute resources, managing state, handling failures, and coordinating workflows. The parallel isn't machine learning; 𝗶𝘁'𝘀 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. Teams already handling distributed systems are perfectly positioned for multi-agent AI. 𝗔𝗖𝗥: 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗔𝗣? Intercom's research (https://bit.ly/4jTNA8O) reveals a fundamental tradeoff mirroring distributed systems' CAP theorem (https://bit.ly/4lbZCeV):  • 𝗔𝗴𝗲𝗻𝗰𝘆 (autonomy): Independent decision-making  • 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (predictability): Constraining agent behavior  • 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (consistency): Consistent results across executions High-agency agents achieve only 20-30% reliability on complex tasks. But constrained, step-based agents hit 60%+ reliability—the enterprise sweet spot. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰'𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶 𝗔𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 Their Research feature (https://bit.ly/408qpjZ) demonstrates distributed systems principles:  • 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝗪𝗼𝗿𝗸𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻: Lead agent coordinates, subagents execute—like API gateways + microservices  • 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Multiple agents with separate context windows—horizontal scaling of cognitive work  • 𝗦𝘁𝗮𝘁𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Memory persistence + checkpointing for failure recovery  • 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀: 15x token usage requires careful allocation—distributed compute cost management 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Multi-agent systems excel at:  • Breadth-first research (90.2% improvement)  • Information synthesis across context windows  • Tool-heavy integrations with specialized agents 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Enterprise AI success comes from 𝗮𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗼 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻. Microservices teams are already equipped to win. Get started with OSS AWS Strands Multi-agent (https://bit.ly/4e9hrsO) or Managed Amazon Web Services (AWS) Bedrock Agents (https://go.aws/43Z97qH).