Using AI For Task Management

If you’re building AI agents that need to work reliably in production, not just in demos, this is the full-stack setup I’ve found useful From routing to memory, planning to monitoring, here’s how the stack breaks down 👇 🧠 Agent Orchestration → Agent Router handles load balancing using consistent hashing, so tasks always go to the right agent → Task Planner uses HTN (Hierarchical Task Network) and MCTS to break big problems into smaller ones and optimize execution order → Memory Manager stores both episodic and semantic memory, with vector search to retrieve relevant past experiences → Tool Registry keeps track of what tools the agent can use and runs them in sandboxed environments with schema validation ⚙️ Agent Runtime → LLM Engine runs models with optimizations like FP8 quantization, speculative decoding (which speeds things up), and key-value caching → Function Calls are run asynchronously, with retry logic and schema validation to prevent invalid requests → Vector Store supports hybrid retrieval using ChromaDB and Qdrant, plus FAISS for fast similarity search → State Management lets agents recover from failures by saving checkpoints in Redis or S3 🧱 Infrastructure → Kubernetes auto-scales agents based on usage, including GPU-aware scheduling → Monitoring uses OpenTelemetry, Prometheus, and Grafana to track what agents are doing and detect anomalies → Message Queue (Kafka + Redis Streams) helps route tasks with prioritization and fallback handling → Storage uses PostgreSQL for metadata and S3 for storing large data, with encryption and backups enabled 🔁 Execution Flow Every agent follows this basic loop → Reason (analyze the context) → Act (use the right tool or function) → Observe (check the result) → Reflect (store it in memory for next time) Why this matters → Without a good memory system, agents forget everything between steps → Without planning, tasks get run in the wrong order, or not at all → Without proper observability, you can’t tell what’s working or why it failed → And without the right infrastructure, the whole thing breaks when usage scales If you’re building something similar, would love to hear how you’re thinking about memory, planning, or runtime optimization 〰️〰️〰️〰️ ♻️ Repost this so other AI Engineers can see it! 🔔Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources 📙I write long-form technical blogs on substack, if you'd like deeper dives: https://lnkd.in/dpBNr6Jg

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

I killed an AI agent that had been running for 45 minutes. Replaced it with one that finished the same task in 10. Here is what I learned about picking the right agent for the job. Context: I run a local AI stack at home — Qwen3.5 122B on my AMD Ryzen AI MAX+. All my agents run through ACP (Agent Communication Protocol): a protocol that lets you swap, chain, and route between different coding agents like opencode, pi, codex, or gemini. I needed to rebuild a workout app frontend. Simple React files. I spun up opencode. 45 minutes later: GPU pegged at 98%, nothing shipped. Why? opencode is built for complex work. It explores your codebase, creates a plan, breaks it into subtasks, reviews its own output, iterates. That loop is genuinely powerful: For multi-file refactors, architecting new features, reviewing PRs. For writing simple html files? Massive overkill. So I killed it and switched to pi. 10 minutes. File written. Committed. Server running. Pi does not plan. It does not explore. It reads the task, writes the output, and exits. Lean loop. Zero ceremony. Same 122B model underneath both agents. Completely different behaviour on top. That is the real insight about ACP: The protocol is not the intelligence. The agent is. Most people think about AI agents as a single thing — pick the smartest one and use it for everything. But intelligence is only half the equation. Behaviour matters too. ACP lets you match agent behaviour to task complexity: - Simple file task: pi (fast, direct, no overhead) - Complex codebase work: opencode (thorough, iterative) - Research + writing: claude or gemini - Background monitoring: haiku (cheap, does not block the main model) Use a scalpel when you need a scalpel. Do not send a surgeon to hang a picture frame.

AI coding agents can coordinate now. (but they still can't learn from past work) Multi-agent coordination in Claude Code has come a long way. You can spawn teams, assign tasks, share context between agents. But there's a deeper problem that coordination alone doesn't solve: Every session starts from scratch. Your agents figured out the best way to decompose a migration task last week? Gone. The routing pattern that worked for your security reviews? Not stored anywhere. The context from yesterday's debugging session? Evaporated. Coordination without memory is like a team with perfect communication but collective amnesia. Claude-Flow by Reuven Cohen addresses this. It's a multi-agent orchestration framework for Claude Code that adds what native tooling is still missing: agents that learn, remember, and improve over time. Here's the core idea: Every time a task completes successfully, the pattern is stored, which agents were involved, how the task was decomposed, what strategies worked best. Over time, the router learns to match new tasks to the agents and approaches that have historically performed best, with 89% routing accuracy based on learned patterns. But here's what I find most interesting: It uses HNSW-based vector memory that persists across sessions. Instead of every agent reasoning from scratch, they can retrieve relevant past work, previous decisions, architectural context, debugging findings and build on it. This is the same shift we saw from naive RAG to agent memory. Moving from stateless retrieval to a system that actually accumulates knowledge over time. On the cost side, Claude-Flow can route subtasks to different LLM providers based on complexity. Your code generation might use a heavier model while documentation uses a lighter one. Teams report 30–50% token reduction from this alone. Getting started is straightforward, install it, connect to Claude Code as an MCP server, and you get 60+ specialized agents directly in your existing workflow. Everything is 100% open-source with 14k+ stars. I have shared the GitHub repo in the comments!

Stop Chasing AI Hype. Your Best Agentic AI Use Case Is Hiding in Your Biggest Bottleneck If you want to know where AI agents can create a 10x impact, don't look at the latest tech demos. Look for the places your teams can’t catch up — no matter how hard they work. I call this the "Bottleneck Test," a simple 3-step framework to find your best AI use cases. Step 1: IDENTIFY the Chronic Bottleneck Ask: "Where does the work never end?" At one of our clients, this was the engineering team's code review process. They were perpetually behind, not because they were bad at their jobs, but because they were outnumbered by the sheer volume of pull requests. The bottleneck was structural. This isn't just a tech problem. It happens everywhere: • Legal teams buried in standard contract reviews. • Finance departments manually reconciling thousands of invoices. • Marketing teams trying to qualify an endless flood of inbound leads. Step 2: QUALIFY the Use Case The best candidates for an AI agent are tasks that are repetitive, rules-based, and have clear success metrics. For our client, code review was perfect. It required checking against internal standards, security policies, and documentation—all data an AI agent could be trained on. Step 3: PILOT the Agent Our client introduced an AI code review agent as a pilot. It didn’t replace engineers. It augmented them. The agent handled the routine work—flagging common errors, checking for compliance, and summarizing changes—freeing up senior engineers to focus on complex architectural issues. The results were transformative: • Cycle times dropped by 40%. • Code quality and security posture improved. • Engineers could finally focus on meaningful work. Your roadmap for Agentic AI shouldn't be a list of technologies to try. It should be a list of your most critical business bottlenecks to solve. What is the biggest "work never ends" bottleneck in your organization? Share in the comments—let's discuss which ones are prime candidates for an AI agent. Zinnov Dipanwita Ghosh Namita Adavi ieswariya k Arpit Bhatia Amita Goyal Karthik Padmanabhan Mohammed Faraz Khan Komal Shah Ashveen Pai Hani Mukhey Anandhu Ajith Vyas Vandna Lal

Execution doesn’t break because people are unskilled or unmotivated. It breaks because outdated systems quietly create friction slow decisions, repetitive tasks, scattered workflows, and endless context switching. AI removes that friction. By automating the busywork and streamlining execution, AI gives teams the freedom to focus on work that actually moves the business forward. The results are faster cycles, clearer priorities, and fewer operational blind spots. Here are 5 ways AI clears execution bottlenecks and accelerates momentum: 1. Automates repetitive tasks: AI handles routine, time-consuming work reporting, data entry, scheduling, documentation so human effort isn’t drained on admin. This instantly frees hours that can be reallocated to high-impact execution. 2. Eliminates decision delays: AI consolidates information, highlights options, and surfaces insights faster than traditional processes. Leaders spend less time gathering data and more time making informed decisions. 3. Reduces context switching: AI centralises tools, information, and workflows. Instead of juggling five platforms or re-creating lost progress, teams work in a single flow dramatically reducing cognitive load and execution drag. 4. Standardises workflows: AI brings consistency. Whether it’s onboarding, content creation, customer responses, or approvals, AI-driven frameworks ensure that processes are carried out the same way every time reducing errors and speeding execution. 5. Flags operational gaps early: AI monitors patterns, bottlenecks, delays, and anomalies in real time. Instead of reacting after something breaks, teams get proactive alerts that keep execution tight and predictable. Companies that leverage AI for operational flow execute faster and win faster. If you’re not using AI to streamline your systems, you’re already behind. #AI #Productivity #DigitalTransformation #Execution #FutureOfWork

These frameworks are 50+ years old. Most people still cannot make them work!! The Eisenhower Matrix? 1954. The Pareto Principle? 1896. Warren Buffett's 5/25 Rule? Decades old. We have read about them. We have studied them. Probably tried to implement some of these. But when Monday hits and your inbox explodes? When everything is marked urgent? When your calendar is back-to-back and your team needs answers now? Those frameworks stay in the bookmark folder. The problem was never knowledge. It was bandwidth. That is where AI changes everything. Not by replacing these frameworks. By finally making them usable. Here is how AI can help (important: you have to give it your real context - your tasks, goals, constraints, thought process etc.): 𝗖𝗹𝗮𝗿𝗶𝗳𝘆 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝗲𝘀 𝗘𝗶𝘀𝗲𝗻𝗵𝗼𝘄𝗲𝗿 𝗠𝗮𝘁𝗿𝗶𝘅 - Paste your task list and ask AI to sort it into the four quadrants based on your role and goals. 𝗣𝗮𝗿𝗲𝘁𝗼 𝟴𝟬/𝟮𝟬 - Ask AI to spot which activities drive the most results vs where your time actually goes. 𝗕𝘂𝗳𝗳𝗲𝘁𝘁'𝘀 𝟱/𝟮𝟱 𝗥𝘂𝗹𝗲 - Share your goals and ask AI to challenge which 5 really deserve focus. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 𝗥𝗜𝗖𝗘 𝗠𝗲𝘁𝗵𝗼𝗱 - Give AI your projects and ask it to score them on Reach, Impact, Confidence and Effort. 𝗠𝗼𝗦𝗖𝗼𝗪 𝗠𝗲𝘁𝗵𝗼𝗱 - Ask AI to sort deliverables into Must, Should, Could and Won't based on your deadline. 𝗔𝗕𝗖𝗗𝗘 𝗠𝗲𝘁𝗵𝗼𝗱 - Paste tomorrow's to-do list and ask AI to label each task A to E with a reason. 𝗔𝗰𝘁 𝘄𝗶𝘁𝗵 𝗳𝗼𝗰𝘂𝘀 𝗘𝗮𝘁 𝗧𝗵𝗮𝘁 𝗙𝗿𝗼𝗴 - Ask AI which task you will procrastinate on and to break it into 3 starting steps. 𝗧𝗶𝗺𝗲 𝗕𝗹𝗼𝗰𝗸𝗶𝗻𝗴 - Ask AI to draft an ideal week around your priorities and energy levels. 𝗕𝗮𝘁𝗰𝗵𝗶𝗻𝗴 - Ask AI to group tasks by type and suggest which belong in the same time block. The thinking stays human. The execution gets faster. This is exactly what we teach in my AI Accelerator — how to use AI to apply strategies smart leaders have known for decades but never had the bandwidth to execute. Because knowing the framework was never the hard part. Using it consistently was. Now you can. AI is one of the core pillars in my Portfolio Career Accelerator. Cohort 1 is working through this right now. If you want to join Cohort 2, we start April 27. Learn about the accelerator here: https://lnkd.in/gidkFZVR or in my Featured section Image credit: Stephanie Hills, Ph.D. - follow her, she is amazing!

Stupid simple, but this is the most valuable AI skill I've built. Every Monday at 8am, Claude audits how I worked the past seven days and tells me exactly what I should stop doing manually. I don't have to think about it. It just runs. Here's the full setup: What it does Scans the software I use every day (Slack, Gmail, Granola, Jira, Notion, and Salesforce) from the past week. One job: find patterns in how I work that should become automated skills instead of things I keep doing by hand. Comes back with a ranked list and one concrete build recommendation for the week. Specific, not vague. It'll say "4 similar post-demo follow-up emails sent to pharma accounts this week" not "you email a lot." How to set it up in Cowork: Step 1: Connect your tools In Cowork, go to Connectors and add whatever you actually use: Web connectors: Gmail, Slack, Jira, Hubspot MCP connectors: Salesforce, Granola CLI: Google Docs, Sheets More tools connected means better pattern recognition. Start with whatever you live in most or what's already pre-built by Claude. Step 2: Create the skill In Cowork, open Skills and create a new one called "weekly-skill-discovery". Paste this as your instructions (simplified to get under LinkedIn character limit): Your job: find work I did repeatedly this past week that I shouldn't be doing manually next week. Scan the past 7 days across all connected tools: Granola: recurring decisions I'm explaining the same way across calls, follow-up actions I keep doing manually, anything I said in 2+ meetings that sounds templated. Slack: messages that follow a template, threads where I'm answering the same question to different people, anything I'm rewriting from scratch each time. Gmail: emails with similar structure sent to multiple people, threads where I'm the bottleneck. Jira: ticket types I write from scratch that follow a pattern, recurring status updates. For each candidate output: what it is, where you saw it, a specific example from this week, rough time saved per week. Rank by frequency, time cost, and leverage over time. End with one recommendation: "If you only build one skill this week, build X because Y. Here's a draft prompt to get started." Rules: specific beats vague. Patterns only, 2+ instances. Nothing significant? Say so. Don't pad. Under 500 words. I'm reading this Monday morning. Step 3: Schedule it Hit /schedule in Cowork. Set it for Monday morning. Hit Run Now to test it before you walk away. That's the whole setup. Ten minutes to get running. And it compounds: each week I either build the recommended skill or decide it's not worth it. But I'm making that decision consciously instead of letting inefficiency quietly pile up. Two months in, I've gone from 3 automated workflows to 14. I didn't build most of them intentionally. This found them for me. Comment "skill" and I'll send you the full file to drop straight into Cowork, no copy-pasting required.

🚀 Opportunities with Intelligent Routing: Exploring the vLLM Semantic Router In the article, I walk through how I leveraged the vLLM Semantic Router — a cutting-edge “Mixture-of-Models” (MoM) router that intelligently dispatches requests based on semantic understanding of the task. ➡️ In this proof of concept, I specifically built out a routing pipeline using Qwen 3B and ModernBERT: - ModernBERT for lightweight classification / prompt-understanding of task intent - Qwen 3B for richer responses where the task demands broader generation This hybrid setup unlocked improved efficiency (faster / cheaper routing) and stronger accuracy (matching the best model to each request) in our limited data/compute sandbox. 📌 Why this matters Here are some of the key benefits I highlight in the article: ✅  Smarter model utilisation – Rather than always “fire the biggest model”, the router picks the right model for each request, maximising performance and cost-effectiveness. ✅ Reduced latency & cost – By delegating simpler tasks to lighter models (ModernBERT) and reserving heavy models (Qwen 3B) for the hard stuff, end-to-end latency drops and compute cost goes down. Improved accuracy / relevance – Semantic routing helps ensure the task is handled by a model tuned for the right domain (e.g., coding vs summarisation vs Q&A) which increases quality. ✅ Modular, future-proof architecture – You can plug in new models (or replace existing ones) into the router architecture, sidestepping monolithic “one-model-fits-all” limitations. ✅ Enterprise-ready features – The vLLM Semantic Router also brings in capabilities like domain-aware system prompts, semantic caching, PII detection, prompt guard, distributed tracing. ✅ Better tool / prompt management – The router can intelligently select relevant tools and system prompts based on classification of input, reducing wasted prompt tokens and improving tool-utilisation. 🔗 Check out the repository For full code, architecture diagrams, examples and docs: the vLLM Semantic Router repo is here → https://lnkd.in/gwFX8HVT  Feel free to browse the “examples” folder and the “bench” folder to see sample config and metrics. If you’re working on large-language models / inference infrastructure / cost-efficient model deployment, this is a project worth exploring. I’m hiring Machine Learning and Generative AI engineers! If you’re passionate about LLMs and applied AI, I’d love to connect. Disclaimer: The views and opinions expressed here are my own and do not represent those of my employer or any affiliated organization.

𝐋𝐞𝐚𝐝𝐞𝐫𝐬: 𝐭𝐡𝐞𝐫𝐞’𝐬 𝐚𝐧 𝐢𝐧𝐯𝐢𝐬𝐢𝐛𝐥𝐞 𝐭𝐚𝐱 𝐝𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠. (It’s not headcount. It’s not tech. It’s delay.) Every unnecessary sign-off. Every unclear approval path. Every well-meaning gatekeeper... → adds friction to your most valuable workflows. And as a leader, you don’t always see it—until the cost shows up in burnout, missed deadlines, and stalled growth. But what if AI could help you find (and fix) the 10% of roles responsible for 70% of the delay? 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐝𝐚𝐭𝐚-𝐛𝐚𝐜𝐤𝐞𝐝 𝐀𝐈 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠 𝐦𝐨𝐯𝐞 𝐟𝐚𝐬𝐭𝐞𝐫: Industry Metrics (you’ll want to screenshot this): • 10–30% of operating costs = tied up in inefficiency • Knowledge workers lose 9.3 hrs/week on unnecessary wait time • AI/automation can slash indirect costs by 15–20% within 12–18 months 𝐓𝐡𝐞 4-𝐒𝐭𝐞𝐩 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐔𝐧𝐛𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐑𝐞𝐜𝐢𝐩𝐞.. 1. Slice 10% Pick 2–3 roles in your highest-value workflow. The "thin slice" gives you 70% of the insight of a full-scale audit—with 10% of the effort. 2. Diagnose with AI Ask ChatGPT: "Estimate weekly hours each role spends on approvals. Flag any over 20%." This spots the "guardian paradox"—where well-meaning protectors become bottlenecks. 3. Pilot a Fix—Fast (Think: Plan → Do → Check → Act) • Plan: Use AI to pinpoint the “Form Lord” or “Access Czar” in your workflow • Do: Pilot a self-service option, automation, or simplified approval path • Check: Re-measure how long the process takes • Act: If it works, scale the fix across similar teams You don’t need a six-month project. You need one high-friction step, one experiment, one fast win. 4. Quantify the ROI Time saved × fully loaded rate = the case your CFO will love 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮 𝐚𝐬 𝐚 𝐥𝐞𝐚𝐝𝐞𝐫: • 60% cycle-time gains—without ripping out systems • 15–20% cost savings—without headcount cuts • Become the leader who brought AI with ROI • Turn bottleneck bosses into flow enablers—watch morale soar This week’s challenge: Pick one high-friction process. Run the 10% slice through an LLM. Pilot one fix. Track the before/after. Then post your story with #IntelligentWorkflows. Leaders go first. Let’s show the org how it’s done. ♻️ Repost if this gave you something to think about.