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

The gap between AI-native operators and everyone else is widening quickly. And if you're only using ChatGPT, you're not embracing a truly AI-native way of working. Anthropic recently released Claude Cowork, which makes AI agents accessible to the 'rest of us' (aka people who aren't programmers). They *just* announced connections to Google Drive, Gmail, DocuSign & more. Cowork can plan & execute multi-step tasks like: 1. Create or edit local docs, spreadsheets or presentations 2. Organize folders to clean up space on your hard drive 3. Read Google Workspace files & emails 4. Manage your calendar In today's newsletter: AI builder Justin Norris unpacks what Claude Cowork means for you & presents a vision for AI-native knowledge work. Read the full deep dive here: https://lnkd.in/ei92-QJH Where Justin is landing today: - Uses a chat-based assistant as a personal Chief of Staff (he shares his exact prompt & routine). He checks in with the CoS at the start & end of the day. - Pair that with an execution-focused AI agent that has access to systems & sources (Claude Cowork fits in here). - Use AI assistants to absorb Tier 1 work like questions, lookups, repetitive requests, and first drafts. - With your extra time, re-focus on what AI can't do: (a) gather context, (b) decide which problems are worth solving, (c) plan projects, (d) anticipate org dynamics, & (e) communicate w/ your team. None of this is seamless. There's still copying & pasting. But you can already operate much closer to the future than it might appear.

"A Multifaceted Vision of the Human-AI Collaboration: A Comprehensive Review" provides some interesting and useful insights into effective Humans + AI work, drawn from across the literature. Some of the specifics insights in the paper: 🧭 Use the five-cluster framework to tailor collaboration depth. The framework defines five types of human-AI collaboration: (1) Humans as optional tools, (2) Consensus-based coordination, (3) Asynchronous collaboration, (4) Humans and AI as co-agents, and (5) Humans directing AI. Choose the type based on your task: use cluster 1 for personalization (e.g. recommender systems), cluster 2 for group decision-making, clusters 3 and 4 for task co-execution, and cluster 5 when human judgment must lead the process. 🧠 Let humans steer the learning loop. Design workflows where human feedback isn't just collected but actively changes the model. Show users how their input influences outcomes, and ensure systems update based on their corrections—failing to do so erodes trust and engagement fast. 🔄 Support iterative improvement through clear feedback cycles. Let users provide input at multiple points in the workflow—before, during, and after AI output. Use real-time feedback, editable suggestions, and memory-based personalization (e.g., saving past preferences) to refine collaboration with each loop. 📣 Grant users communication initiative. Don’t restrict user interaction to predefined prompts—enable them to ask questions, challenge decisions, or suggest new directions. This increases user autonomy, supports trust, and improves performance in both individual and group collaboration. 🛠️ Customize AI outputs to user-specific contexts. Embed features that allow tailoring of recommendations, predictions, or decisions to individual preferences or needs. For example, let users tweak rehabilitation goals in health tools or input content preferences in recommender systems. 🤖 Use AI as an impartial coordinator in group settings. In scenarios with multiple human participants—such as disaster planning or multi-user workflows—deploy AI to synthesize input, allocate tasks, and reduce bias. Ensure the system is transparent and users can reject or adjust AI decisions. 🔐 Prioritize human-centered design values. Build systems that are transparent (explain why outputs were generated), trustworthy (learn from user feedback), accessible (usable by non-experts), and empowering (give users control over high-level behavior). These are essential for lasting, ethical collaboration.

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.

In every conversation with project/procurement leaders, the same frustration arises: 𝐍𝐨 𝐨𝐧𝐞 𝐬𝐭𝐢𝐜𝐤𝐬 𝐭𝐨 𝐭𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐬𝐮𝐟𝐟𝐞𝐫. I’ve seen this happen firsthand—delays don’t happen in isolation. It’s never just the vendor, the client, or the procurement team. It’s one of those collective contributions! Some of the many reasons: - Albeit under pressure, Vendors commit to terms without 100% clarity. - Low focus on planning at MSMEs adds to the noise. - Vendors portray on-ground situations much better than they really might be. - Any mid-way changes by the clients, shifting expectations and complicating the problem statement for the vendors further. - Vendors scramble with last-minute acceleration and resource constraints. - Internal teams juggle misalignments, leading to reactive decisions. In project procurement from MSME vendors, in my view, the biggest aspect that leads to delays is a lack of transparency and visibility of how the work is progressing on the vendor side. For instance, on the vendor side— any gaps in planning for the procurement of raw materials and bought-out items lead to chaos at the last minute. Inefficiencies in capturing real inputs in current formats—spreadsheets, emails, scattered approvals—only add to the chaos. Further, the lack of authentic data makes it difficult to address real issues. What happens next? 𝐅𝐢𝐫𝐞𝐟𝐢𝐠𝐡𝐭𝐢𝐧𝐠, 𝐜𝐨𝐬𝐭 𝐨𝐯𝐞𝐫𝐫𝐮𝐧𝐬, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐝𝐞𝐥𝐚𝐲𝐬 𝐭𝐡𝐚𝐭 𝐧𝐨 𝐨𝐧𝐞 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐞𝐝 𝐟𝐨𝐫! At Venwiz, we are leveraging technology and have developed a Milestone Management Tool (MMT) to capture real-time information and reduce human dependency, tracking jobs at multiple vendor locations. The on-ground team is responsible for capturing raw data from different sites. However, all the metrics used for project tracking are calculated using our Milestone Management Tool (MMT)—which adds to the authenticity and reliability of the data. Our core focus is on actively preventing (and reducing) delays by understanding the root causes. In my opinion, the best procurement leaders don’t just manage vendors—they orchestrate the entire project ecosystem with data and transparency. How do you tackle shifting timelines in your projects? #Manufacturing #CapEx #Procurement #VendorManagement #Automation

🚀 Excited to share my latest Fortune column on truly groundbreaking academic work from my co-authors Professor Karim Lakhani and Fabrizio Dell'Acqua at Digital Data Design Institute at Harvard (D^3), where I serve as an executive fellow. This remarkable field experiment with 776 Procter & Gamble professionals fundamentally challenges what we thought we knew about teamwork. The research reveals the emergence of the "cybernetic teammate"—AI that doesn't just assist but actively participates in collaboration. Three breakthrough findings: 1. AI Can Replicate Team Benefits Individuals working with AI achieved nearly 40% performance gains—matching traditional two-person teams. AI is providing the same collaborative benefits we've long attributed to human teamwork. 2. Cross-Functional AI Teams Generate Breakthrough Innovation AI-augmented cross-functional teams were 3x more likely to produce top 10% solutions. This isn't marginal improvement—it's a multiplicative effect that neither human-only teams nor AI-enabled individuals could achieve alone. 3. AI Breaks Down Silos (For Real This Time) R&D specialists with AI proposed commercially viable solutions. Commercial professionals developed technically sound approaches. AI acted as a bridge, enabling each team member to think holistically across functions—achieving the "silo breaking" that leaders have struggled to accomplish through org chart reshuffles. Bonus finding: AI collaboration increased positive emotions by 64% in teams. This isn't cold, mechanical work—it's energizing and engaging. At Seven2, we're translating this research into practice with our portfolio companies, building these AI-augmented cross-functional teams to drive innovation and competitive advantage. This is the future of collaborative work—not AI replacing humans, but human-AI ensembles that combine the best of both worlds. Read the full analysis: https://lnkd.in/ef3f3pED #AI #Innovation #HBS #D3Institute #FutureOfWork #PrivateEquity #TeamDynamics

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!

What was Atlassian’s Modern Work Coach, Mark Cruth, teaching in Philly? The 3 steps to Team-First AI: how to use AI for team coordination, not just personal productivity. I caught up with Mark on the Philly leg of his Atlassian Community roadshow. The last time we were in the same room was 3 years ago in Lisbon at Running Remote. And once again, he showed up with his trademark high energy and ability to connect with a group. His session centered on a key question every leader should be asking in 2025: How can teams use AI to enable team coordination, not just personal productivity? Mark shared Atlassian’s Team-First AI framework: 1️⃣ Start with knowledge 2️⃣ Map the journey 3️⃣ Redesign the team As part of "redesign the team" - he reinforced something that we've been doing when we guide cross-functional teams through the development of their Team Working Agreements (TWA). A first step in a Team Working Agreement is clarifying goals and roles. Within that first step, we've added the question, "What is the role of AI on this team?" Atlassian has seen the value in doing this first-hand. 🔥Mark shared a brilliant example: Atlassian built an AI agent (using Rovo) called the Decision Director. Trained by one of their internal DACI experts, it helps any team, at any time, structure clear a decision-making framework. This isn’t just task automation. It’s team augmentation. So when you're redesigning your team, ask: ➡️ Where can AI extend expertise? ➡️ Where can it free up focus time? ➡️ How do we make its role intentional, not accidental? → What role has your team intentionally given to AI? Shout outs: 🙌 To Keira Gallagher, CMP from Appfire and Maya S. and the rest of the Philly Atlassian Community for organizing! 🙌 To Molly Sands, PhD and her team for the research Mark quoted. 🙌 To Annie Dean for the legacy she left from her leadership of Team Anywhere.

📊 Detailed Project Status Dashboard A Detailed Project Status Dashboard is not just a report—it is your decision engine. Studies show that 70% of projects fail due to poor visibility and communication, not lack of effort. A powerful dashboard solves this instantly by turning raw data into clear, actionable insights. High-Quality Project Management Templates & Documents: at: https://lnkd.in/dCGqF98z 🚀 Why It Matters A well-designed dashboard helps you: • 📌 Track real-time project health (RAG Status) • 📅 Monitor schedule vs. actual progress • 💰 Control budget performance • ⚠️ Identify top risks & issues early Organizations using structured dashboards report up to 35% faster decision-making and 28% better project success rates. 🧩 Key Components of a Powerful Dashboard 🔴 RAG Status (Red, Amber, Green) Instantly shows project health. If 1 out of 5 projects turns red, leadership can act immediately. 📆 Project Schedule Snapshot Highlight critical milestones and delays. Research shows projects with milestone tracking are 40% more likely to finish on time. ⚠️ Top 5 Risks & Issues Focus only on what matters most. Leaders don’t need 50 problems—they need the top 5 that can break the project. ✅ Activities Done vs Open A clear productivity tracker. Teams with visibility into task completion improve output by 20–25%. 📈 Budget & Performance Metrics Compare planned vs actual costs. Poor cost tracking leads to overruns in 45% of projects globally. 💡 The Real Power A dashboard transforms confusion into clarity. Instead of asking: “What is happening in the project?” You start saying: “I know exactly what to fix right now.” That is the difference between an average manager and a high-performing project leader. 💼 Want This Level of Control? If you want dashboards that automatically track your projects, update in real time, and present executive-level insights, then you need a structured system—not manual reports. Our High-Quality Project Management Templates & Documents give you: • Ready-to-use dashboards • Automated data tracking • Professional reporting format • Zero confusion, full control 👉 Get started here: https://lnkd.in/dCGqF98z 🔖 #ProjectManagement #Dashboard #ProjectStatus #PMO #Leadership #DataDriven #ProjectPlanning #RiskManagement #Productivity #BusinessGrowth