Strategies for Developing AI Agents

If you’re an AI engineer building multi-agent systems, this one’s for you. As AI applications evolve beyond single-task agents, we’re entering an era where multiple intelligent agents collaborate to solve complex, real-world problems. But success in multi-agent systems isn’t just about spinning up more agents, it’s about designing the right coordination architecture, deciding how agents talk to each other, split responsibilities, and come to shared decisions. Just like software engineers rely on design patterns, AI engineers can benefit from agent design patterns to build systems that are scalable, fault-tolerant, and easier to maintain. Here are 7 foundational patterns I believe every AI practitioner should understand: → 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Run agents independently on different subtasks. This increases speed and reduces bottlenecks, ideal for parallelized search, ensemble predictions, or document classification at scale. → 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Chain agents so the output of one becomes the input of the next. Works well for multi-step reasoning, document workflows, or approval pipelines. → 𝗟𝗼𝗼𝗽 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Enable feedback between agents for iterative refinement. Think of use cases like model evaluation, coding agents testing each other, or closed-loop optimization. → 𝗥𝗼𝘂𝘁𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Use a central controller to direct tasks to the right agent(s) based on input. Helpful when agents have specialized roles (e.g., image vs. text processors) and dynamic routing is needed. → 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Merge outputs from multiple agents into a single result. Useful for ranking, voting, consensus-building, or when synthesizing diverse perspectives. → 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹) 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Allow all agents to communicate freely in a many-to-many fashion. Enables collaborative systems like swarm robotics or autonomous fleets. ✔️ Pros: Resilient and decentralized ⚠️ Cons: Can introduce redundancy and increase communication overhead → 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Structure agents in a supervisory tree. Higher-level agents delegate tasks and oversee execution. Useful for managing complexity in large agent teams. ✔️ Pros: Clear roles and top-down coordination ⚠️ Cons: Risk of bottlenecks or failure at the top node These patterns aren’t mutually exclusive. In fact, most robust systems combine multiple strategies. You might use a router to assign tasks, parallel execution to speed up processing, and a loop for refinement, all in the same system. Visual inspiration: Weaviate ------------ If you found this insightful, share this with your network Follow me (Aishwarya Srinivasan) for more AI insights, educational content, and data & career path.

Now that you’ve selected your use case, designing AI agents is not about finding the perfect configuration but making deliberate trade-offs based on your product’s goals and constraints. You’ll be optimizing for control, latency, scalability, or safety, and each architectural choice will impact downstream behavior. This framework outlines 15 of the most critical trade-offs in Agentic AI to help you build successfully: 1.🔸Autonomy vs Control Giving agents more autonomy increases flexibility, but reduces human oversight and predictability. 2.🔸Speed vs Accuracy Faster responses often come at the cost of precision and deeper reasoning. 3.🔸Modularity vs Cohesion Modular agents are easier to scale. Cohesive ones reduce communication overhead. 4.🔸Reactivity vs Proactivity Reactive agents wait for input. Proactive ones take initiative, sometimes without clear triggers. 5.🔸Security vs Openness Opening up tool access increases capability, but also the risk of data leaks or misuse. 6.🔸Memory Depth vs Freshness Deep memory helps with long-term context. Fresh memory improves agility and faster decision-making. 7.🔸Multi-Agent vs Solo Agent Multi-agent systems bring specialization but add complexity. Solo agents are easier to manage. 8.🔸Cost vs Performance More capable agents require more tokens, tools, and compute, raising operational costs. 9.🔸Tool Access vs Safety Letting agents access APIs boosts functionality but can lead to unintended outcomes. 10.🔸Human-in-the-Loop vs Full Automation Humans add oversight but slow things down. Full automation scales well but may go off-track. 11.🔸Model-Centric vs Function-Centric Model-based reasoning is flexible but slower. Function calls are faster and more predictable. 12.🔸Evaluation Simplicity vs Real-World Alignment Testing in a sandbox is easier. Real-world tasks are messier, but more meaningful. 13.🔸Static Prompting vs Dynamic Planning Static prompts are stable. Dynamic planning adapts better, but adds complexity. 14.🔸Generality vs Specialization General agents handle a wide range of tasks. Specialized agents perform better at specific goals. 15.🔸Local vs Cloud Execution Cloud offers scalability. Local execution gives more privacy and lower latency. These kinds of decisions shape results of your AI system, for better… or worse. Save this for reference and share with others. #aiagents #artificialintelligence

We’re entering an era where AI isn’t just answering questions — it’s starting to take action. From booking meetings to writing reports to managing systems, AI agents are slowly becoming the digital coworkers of tomorrow!!!! But building an AI agent that’s actually helpful — and scalable — is a whole different challenge. That’s why I created this 10-step roadmap for building scalable AI agents (2025 Edition) — to break it down clearly and practically. Here’s what it covers and why it matters: - Start with the right model Don’t just pick the most powerful LLM. Choose one that fits your use case — stable responses, good reasoning, and support for tools and APIs. - Teach the agent how to think Should it act quickly or pause and plan? Should it break tasks into steps? These choices define how reliable your agent will be. - Write clear instructions Just like onboarding a new hire, agents need structured guidance. Define the format, tone, when to use tools, and what to do if something fails. - Give it memory AI models forget — fast. Add memory so your agent remembers what happened in past conversations, knows user preferences, and keeps improving. - Connect it to real tools Want your agent to actually do something? Plug it into tools like CRMs, databases, or email. Otherwise, it’s just chat. - Assign one clear job Vague tasks like “be helpful” lead to messy results. Clear tasks like “summarize user feedback and suggest improvements” lead to real impact. - Use agent teams Sometimes, one agent isn’t enough. Use multiple agents with different roles — one gathers info, another interprets it, another delivers output. - Monitor and improve Watch how your agent performs, gather feedback, and tweak as needed. This is how you go from a working demo to something production-ready. - Test and version everything Just like software, agents evolve. Track what works, test different versions, and always have a backup plan. - Deploy and scale smartly From APIs to autoscaling — once your agent works, make sure it can scale without breaking. Why this matters: The AI agent space is moving fast. Companies are using them to improve support, sales, internal workflows, and much more. If you work in tech, data, product, or operations — learning how to build and use agents is quickly becoming a must-have skill. This roadmap is a great place to start or to benchmark your current approach. What step are you on right now?

You don’t become an expert Agentic AI developer by just learning prompts or calling an API. To build 𝘳𝘦𝘢𝘭 AI agents, you need to master a cross-disciplinary skillset — from system design and semantic search to context management, deployment, and continuous learning. I put together this visual: 𝗧𝗼𝗽 𝟱𝟬 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 — the roadmap I wish I had when I started diving into building intelligent, autonomous agents. Here are some patterns I’ve observed: 1. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵, 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝗮𝗻𝗱 𝗥𝗔𝗚 are non-negotiable for scalable context retrieval.     2. 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 becomes essential when you go beyond a single use case.         3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗮𝗻𝗱 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 are what differentiate a generic chatbot from an adaptive expert.     4. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗯𝗶𝗮𝘀 𝗺𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 make your system trustworthy and resilient.     5. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽, 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, and 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 bring it all into production.    If you’re serious about building in this space, treat this less like a checklist—and more like a curriculum. What would 𝘺𝘰𝘶 add to this list? And what are you focusing on right now?

I just finished reading three recent papers that every Agentic AI builder should read. As we push toward truly autonomous, reasoning-capable agents, these papers offer essential insights, not just new techniques, but new assumptions about how agents should think, remember, and improve. 1. MEM1: Learning to Synergize Memory and Reasoning Link: https://bit.ly/4lo35qJ Trains agents to consolidate memory and reasoning into a single learned internal state, updated step-by-step via reinforcement learning. The context doesn’t grow, the model learns to retain only what matters. Constant memory use, faster inference, and superior long-horizon reasoning. MEM1-7B outperforms models twice its size by learning what to forget. 2. ToT-Critic: Not All Thoughts Are Worth Sharing Link: https://bit.ly/3TEgMWC A value function over thoughts. Instead of assuming all intermediate reasoning steps are useful, ToT-Critic scores and filters them, enabling agents to self-prune low-quality or misleading reasoning in real time. Higher accuracy, fewer steps, and compatibility with existing agents (Tree-of-Thoughts, scratchpad, CoT). A direct upgrade path for LLM agent pipelines. 3. PAM: Prompt-Centric Augmented Memory Link: https://bit.ly/3TAOZq3 Stores and retrieves full reasoning traces from past successful tasks. Injects them into new prompts via embedding-based retrieval. No fine-tuning, no growing context, just useful memories reused. Enables reasoning, reuse, and generalization with minimal engineering. Lightweight and compatible with closed models like GPT-4 and Claude. Together, these papers offer a blueprint for the next phase of agent development: - Don’t just chain thoughts; score them. - Don’t just store everything; learn what to remember. - Don’t always reason from scratch; reuse success. If you're building agents today, the shift is clear: move from linear pipelines to adaptive, memory-efficient loops. Introduce a thought-level value filter (like ToT-Critic) into your reasoning agents. Replace naive context accumulation with learned memory state (a la MEM1). Storing and retrieving good trajectories, prompt-first memory (PAM) is easier than it sounds. Agents shouldn’t just think, they should think better over time.

𝐇𝐨𝐰 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐅𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡: 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥 9-𝐒𝐭𝐞𝐩 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 Building AI agents isn’t just for simple demos. It’s about combining strategy, architecture, and smart tools. Here’s the practical playbook I use step by step: 1) 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐀𝐠𝐞𝐧𝐭’𝐬 𝐑𝐨𝐥𝐞 𝐚𝐧𝐝 𝐆𝐨𝐚𝐥 ↳   What will your agent do? ↳   Who is it helping? ↳   What kind of output will it generate? ↳   Example: An AI agent that analyzes project specs, reviews historical bids, and generates optimized bid proposals. 2) 𝐃𝐞𝐬𝐢𝐠𝐧 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐈𝐧𝐩𝐮𝐭 & 𝐎𝐮𝐭𝐩𝐮𝐭 ↳   Use Pydantic or JSON schemas for structured input. ↳   Make sure your agent only receives valid data. ↳   Avoid messy parsing think clean APIs. ↳   Example tools: Pydantic, JSON Schema, LangChain Output Parsers. 3) 𝐏𝐫𝐨𝐦𝐩𝐭 𝐚𝐧𝐝 𝐓𝐮𝐧𝐞 𝐭𝐡𝐞 𝐀𝐠𝐞𝐧𝐭’𝐬 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫 ↳   Start with role-based system prompts. ↳   Write clear, step-by-step instructions. ↳   Keep tuning your prompts for best results. ↳   Techniques: Prompt Chaining, Output Parsing, Prompt Tuning. 3) 𝐀𝐝𝐝 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐨𝐨𝐥 𝐔𝐬𝐞 ↳   Give your agent access to reasoning frameworks (like ReAct, Tree-of-Thoughts). ↳   Let it chain tools together: search, code, APIs, databases, web scraping. ↳   Example tools: LangChain, Toolkits, ReAct. 5) 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐋𝐨𝐠𝐢𝐜 (𝐢𝐟 𝐧𝐞𝐞𝐝𝐞𝐝) ↳   Use orchestration frameworks if you need teams of agents. ↳   Delegate roles (researcher, reporter, organizer, reviewer). ↳   Enable agents to talk and collaborate. ↳   Example tools: LangGraph, CrewAI, Swarms, OpenAI. 6) 𝐀𝐝𝐝 𝐌𝐞𝐦𝐨𝐫𝐲 𝐚𝐧𝐝 𝐋𝐨𝐧𝐠-𝐓𝐞𝐫𝐦 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 (𝐑𝐀𝐆) ↳   Does your agent need to remember conversations or data? ↳   Integrate Retrieval Augmented Generation (RAG) for real-time context. ↳   Use vector databases for efficient recall. ↳   Example tools: LangChain Memory, Chromadb, FAISS. 7) 𝐀𝐝𝐝 𝐕𝐨𝐢𝐜𝐞 𝐨𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 (𝐎𝐩𝐭𝐢𝐨𝐧𝐚𝐥) ↳   Text-to-speech for agents that talk. ↳   Speech-to-text or OCR for those that listen or see. ↳   Vision models for images, video, and diagrams. ↳   Example tools: TTS, Whisper, CLIP, BLIP. 8) 𝐃𝐞𝐥𝐢𝐯𝐞𝐫 𝐭𝐡𝐞 𝐎𝐮𝐭𝐩𝐮𝐭 (𝐢𝐧 𝐇𝐮𝐦𝐚𝐧 𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐅𝐨𝐫𝐦𝐚𝐭) ↳   Format outputs for humans (reports, emails, dashboards). ↳   Or for machines (APIs, integrations, triggers). ↳   Example tools: LangChain Output Parsers. 9) 𝐖𝐫𝐚𝐩 𝐢𝐧 𝐚 𝐔𝐈 𝐨𝐫 𝐀𝐏𝐈 (𝐎𝐩𝐭𝐢𝐨𝐧𝐚𝐥) ↳   Add a user interface or API for easy access. ↳   Productize your agent for real-world users. Building production-grade AI agents is about getting each step right. Which step are you most excited to tackle next? ♻️ Repost to your LinkedIn followers if you want to see more actionable AI roadmaps. Follow Timothy Goebel for proven AI strategies. #AI #AIAgents #Automation #DataScience #MachineLearning #Innovation