Smart Task Routing Using AI

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.

AI leaders just got a clear playbook from AWS. Agentic AI is not another automation wave. It is a structural shift in how work gets done on Main Street. What matters for small and mid-size firms: What is agentic AI Systems that plan, act, and learn toward a goal. They use your tools, your data, and your policies to get real work done. How it differs from traditional software Agents break goals into steps, self-reflect mid-run, and take actions through APIs. Less rigidity. More outcomes. From agents to outcomes Faster ticket resolution. Cleaner back office. Shorter project cycles. Agents reduce handoffs and close loops automatically. Double down on foundations Unify data. Add a semantic layer. Standardize guardrails. Stable plumbing beats shiny demos. Prepare people for human and AI collaboration Treat agents like teammates with clear roles. Upskill staff to supervise, review, and improve agent work. Embrace flexibility and continuous learning Replace rigid checklists with playbooks that update as conditions change. Reward experiments that produce better outcomes. Build a new governance model Move from task approvals to outcome stewardship. Set goals, thresholds, and escalation rules so agents operate safely within bounds. Start this week Pick one process. Define the goal. List the tool actions an agent can take. Write an acceptance test. Measure cycle time and error rate before and after. Example to copy Process: Tier 1 support triage Goal: route or resolve incoming tickets within 5 minutes Allowed actions: read past tickets, query knowledge base, trigger macros, escalate to Tier 2 Acceptance test: correct routing plus first response quality score above 90 percent Small steps. Real work. Compounding gains.

Why 85% of #AIagent implementations fail? Using single-step thinking when complex problems require orchestrated intelligence. If you want to build production-ready AI agents in 2025, understanding these nine core workflow patterns is no longer optional, It's the foundation of #intelligentautomation. 📌 𝟵 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗰𝗵𝗮𝗻𝗴𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴: 🐙 Sequential Intelligence: ⫸ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗖𝗵𝗮𝗶𝗻𝗶𝗻𝗴 - Decomposes complex tasks into steps where each LLM call processes the output of the previous one, perfect for ChatBot applications and AI agent tools. ⫸ 𝗣𝗹𝗮𝗻 𝗮𝗻𝗱 𝗘𝘅𝗲𝗰𝘂𝘁𝗲 - Creates multi-step plans, executes sequentially, reviews, and adjusts after each task, ideal for business process automation and data pipeline orchestration. ⚡ Parallel Processing: ⫸ 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - Sections tasks or runs them multiple times simultaneously for aggregated outputs, essential for automating evaluations and implementing guardrails. ⫸ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝗪𝗼𝗿𝗸𝗲𝗿 - Central LLM dynamically breaks down tasks and delegates to worker LLMs to synthesize results, powering agentic RAG and coding agents. 🎯 Intelligent Routing: ⫸ 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 - Classifies inputs and directs them to specialized follow-up tasks for separation of concerns, revolutionizing customer support agents and multi-agent debates. ⫸ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿 - One LLM generates responses while another provides evaluation and feedback in a continuous loop, crucial for real-time data monitoring and coding agents. 🧠 Self-Improving Systems: ⫸ 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 - Learns via feedback and self-reflection, reviewing task responses to improve final response quality perfect for complex data monitoring and full-stack app building. ⫸ 𝗥𝗲𝘄𝗼𝗼 - Enhances ReACT with planning and substitution, reducing tokens and simplifying fine-tuning for deep search agents and multi-step question answering. ⫸ 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 - Agents implemented as LLMs performing tasks based on tools feedback in continuous loops, ideal for automating evaluations and implementing guardrails. 🔥 Why These Patterns Change Everything: ✅Transform single AI calls into intelligent orchestrated systems. ✅Enable complex problem-solving that no single LLM could handle. ✅Build self-improving AI systems that get better over time. ✅Scale from simple automation to enterprise-grade intelligence. Whether you're building customer support agents, coding assistants, or complex data processing pipelines, these patterns provide the foundation for AI that actually works in production. This 9 Agentic Workflows that will transform how you build AI Agents today. #agenticworkflow #AIproject

If you're still building the same AI Agent Architectures Here are 4 Modern AI Agent Architectures you can try... I’ve previously shared several common AI Agent workflows and design patterns that received great feedback from many of you. Today, I am sharing 4 advanced AI Agent workflows including code samples to try them out: 📌 Agentic RAG: 1. Query Routing: The User query is directed to the agent for processing and coordination. 3. Task Planning: Agent defines retrieval strategy and selects appropriate sub-agents based on query requirements. 4. Data Retrieval:The agent uses tools like vector search to extract relevant information from the knowledge base. 5. Prompt Optimization: It then merges the retrieved data with the user query and system instructions, applying reasoning to craft an effective prompt for the LLM. 6. Response Generation: The LLM processes this optimized prompt to generate and return the final output. 📌 CodeAct: - User Initiation: The user starts by giving a natural language instruction to the agent. - Agent Planning: The agent plans actions using reasoning, refining based on past observations. - CodeAct Action: The agent generates and sends executable Python code to the environment. - Environment Feedback: The environment executes the code, providing results or errors for the agent to refine actions. 📌 DeepResearch Agents: 1. Query Distribution: The user query is routed to a Lead Agent, which delegates tasks to multiple specialized sub-agents for parallel thinking and processing. 2. Collaboration & Retrieval: Each sub-agent uses its own tools or MCP servers to search, reason, and retrieve data. All agents share memory and tools for coherent collaboration. 3. Aggregation & Output: The Lead Agent aggregates responses from all sub-agents, combines them using reasoning, and delivers a unified result back to the user. 📌 CUA (Computer Using Agents): 1. Perception & Understanding: The user query is routed through the orchestrator. The Vision Language Model observes the browser sandbox to understand the screen and context. 2. Reasoning & Execution: The orchestrator uses the LLM to plan and perform actions using tools, desktop apps, or APIs, guided by the interpreted visual and textual data. 3. Iteration & Memory Use: Outcomes are monitored visually, and memory is used to refine actions iteratively until the task is completed. Code samples in the comments 👇 Not every AI Agent architecture is designed to build and scale enterprise workflows effectively. That’s why we recently launched a cohort focused on building enterprise-grade AI Agents — from PoC to deployment Learn more here: https://lnkd.in/gA3zhcfm The book includes our 5-level Agent Progression Framework for business leaders. 🔗 Book info: https://amzn.to/4irx6nI Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

85% of AI inference costs can be slashed with smart model routing! 🤐 (IBM Research, Oct 2024) Most teams dump every query, simple or complex on their most expensive model. But a GPT-5 style router architecture demands intelligent orchestration that matches model capability to task complexity. Here's what the numbers say 👇 • 70% of cost optimization opportunities missed when teams manually hardcode model choices • Sub-100ms routing decisions possible with semantic analysis (vs. seconds with brute-force approaches) • 95% of GPT-4 performance achievable at just 15% of the cost using intelligent routers • 67% of enterprises now use multi-model GenAI systems (McKinsey, 2025) Smart routing in action looks like this, powered by NVIDIA AI: 🔹 Nemoretriever – lightning-fast RAG retrieval 🔹 Nemotron Nano Vision – image understanding and reasoning 🔹 Flux – instant image generation 🔹 Serper Tools – web browsing and scraping 🔹 Nemotron Nano – conversational orchestration It identifies intent and complexity, then dynamically shifts between modes: fast mode for quick replies, thinking mode for deep reasoning, and fallback mode when resources are tight. This orchestration layer ensures the right specialist handles each task, moving us beyond the one-size-fits-all approach. I have talked enough, you tell me, have you implemented a model routing service for your project yet? If yes, what is your biggest learning? P.S. Follow me, Bhavishya Pandit, for weekly breakdowns on AI cost optimisation and architecture patterns 🔥 #airouting #llm #orchestration #nvidia #genai #aiengineering #enterpriseai