Autonomous Task Management

How far are we from having competent AI co-workers that can perform tasks as varied as software development, project management, administration, and data science? In our new paper, we introduce TheAgentCompany, a benchmark for AI agents on consequential real-world tasks. Why is this benchmark important? Right now it is unclear how effective AI is at accelerating or automating real-world work. We hear statements like: > AI is overhyped, doesn’t reason, and doesn’t generalize to new tasks > AGI will automate all human work in the next few years This question has implications for: - Companies: to understand where to incorporate AI in workflows - Workers: to get a grounded sense of what AI can and cannot do - Policymakers: to understand effects of AI on the labor market How can we begin on it? In TheAgentCompany, we created a simulated software company with tasks inspired by real-world work. We created baseline agents, and evaluated their ability to solve these tasks. This benchmark is first of its kind with respect to versatility, practicality, and realism of tasks. TheAgentCompany features four internal web sites: - GitLab: for storing source code (like GitHub) - Plane: for doing task management (like Jira) - OwnCloud: for storing company docs (like Google Drive) - RocketChat: for chatting with co-workers (like Slack) Based on these sites, we created 175 tasks in the domains of: - Administration - Data science - Software development - Human resources - Project management - Finance We implemented a baseline agent that can web browse and write/execute code to solve these tasks. This was implemented using the open-source OpenHands framework for full reproducibility (https://lnkd.in/g4VhSi9a). Based on this agent, we evaluated many LMs, Claude, Gemini, GPT-4o, Nova, Llama, and Qwen. We evaluated both success metrics and cost. Results are striking: the most successful agent w/ Claude was able to successfully solve 24% of the diverse real-world tasks that it was tasked with. Gemini-2.0-flash is strong at a competitive price point, and the open llama-3.3-70b model is remarkably competent. This paints a nuanced picture of the role of current AI agents in task automation. - Yes, they are powerful, and can perform 24% tasks similar to those in real-world work - No, they can not yet solve all tasks or replace any jobs entirely Further, there are many caveats to our evaluation: - This is all on simulated data - We focused on concrete, easily evaluable tasks - We focused only on tasks from one corner of the digital economy If TheAgentCompany interests you, please: - Read the paper: https://lnkd.in/gyQE-xZG - Visit the site to see the leaderboard or run your own eval: https://lnkd.in/gtBcmq87 And huge thanks to Fangzheng (Frank) Xu, Yufan S., and Boxuan Li for leading the project, and the many many co-authors for their tireless efforts over many months to make this happen.

Introducing MegaAgent: A New Framework for Autonomous Cooperation in LLM Agent Systems ... A new research paper titled "MegaAgent: A Practical Framework for Autonomous Cooperation in Large-Scale LLM Agent Systems" presents a compelling solution to some of the key challenges in the field of large language model agent systems Authored by researchers from esteemed institutions including the National University of Singapore, Shanghai Jiao Tong University, University of California, Berkeley, and South China University of Technology, this paper introduces a framework that enhances the autonomy and cooperation of LLM-based agents. 👉 Context and Importance of Multi-Agent Systems Large language models have demonstrated remarkable capabilities in natural language processing and understanding. However, when deployed in multi-agent systems, they often rely on predefined Standard Operating Procedures (SOPs), limiting their flexibility and scalability. The need for a framework that allows for autonomous cooperation among agents is increasingly critical as we tackle complex, real-world tasks that require dynamic interactions and adaptability. 👉 Key Innovations of MegaAgent The MegaAgent framework introduces several noteworthy innovations that set it apart from existing multi-agent systems: - Autonomous Cooperation Framework: MegaAgent allows agents to operate without predefined SOPs, enabling them to dynamically generate responses based on task requirements. This autonomy fosters creativity and adaptability, crucial for real-world applications. - Dynamic Task Management: The framework employs a hierarchical task-splitting mechanism that allows agents to break down complex tasks into manageable sub-tasks. This not only enhances efficiency but also facilitates collaboration among agents. - Scalability and Performance: MegaAgent can scale up to 590 agents while maintaining effective cooperation. This scalability is essential for applications that require large numbers of agents to work simultaneously, such as policy simulations and complex problem-solving. - Experimental Validation: The effectiveness of MegaAgent has been demonstrated through experiments, including a Gobang game and national policy simulation. The results indicate superior performance compared to existing LLM-MA systems, showcasing its potential for real-world applications. - Future Research Directions: The paper outlines potential avenues for further research, including enhancing agent cooperation and integrating different LLMs to improve efficiency and reduce costs.

LLM agents today suffer from a fundamental knowledge retention problem: every task is treated as a blank slate, with no mechanism to accumulate and reuse expertise from past executions. The agent that successfully navigated a complex hotel booking workflow yesterday has zero memory of that experience when faced with a similar task today. This inability to learn from operational history means repeated failures, redundant reasoning steps, and an inability to handle procedural coordination at scale. Existing approaches like ExpeL, AutoGuide, and AutoManual attempt to address this by extracting experience as flattened textual knowledge from execution traces. While useful for simple heuristics, these text-based representations fundamentally cannot capture the procedural logic of complex subtasks that involve sequential coordination, conditional branching, and state tracking. They also lack any maintenance mechanism, meaning the experience repository degrades over time as redundant and obsolete patterns accumulate, bloating the context window and degrading retrieval quality. AutoRefine (https://lnkd.in/e82wv_PR) introduces a dual-form experience pattern framework that goes beyond text. For complex procedural subtasks, it automatically extracts specialized subagents with independent reasoning and memory, effectively encapsulating multi-step coordination logic as reusable autonomous modules. For simpler strategic knowledge, it extracts skill patterns as guidelines or code snippets. A continuous maintenance mechanism scores patterns on effectiveness, frequency, and precision, then prunes the bottom 20% and merges redundant entries to keep the repository compact. Take a read and keep a lookout for the implementation for a real world scenario soon!

"𝐉𝐮𝐬𝐭 𝐰𝐫𝐢𝐭𝐞 𝐚 𝐠𝐨𝐨𝐝 𝐩𝐫𝐨𝐦𝐩𝐭 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐀𝐠𝐞𝐧𝐭 𝐰𝐢𝐥𝐥 𝐡𝐚𝐧𝐝𝐥𝐞 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠."   If I had a Dollar for every time I heard this, I could fund my own AI Startup. The gap between what people think Agentic Workflows require and what actually scales in production is massive. Let me show you the reality check most teams get after their first deployment. What Looks Simple (Expectation) 1. Prompt-Driven Flow • A strong prompt defines behavior and task execution • Reality check: Prompts drift, edge cases multiply, and ambiguity kills reliability at scale 2. Single Smart Agent • One agent plans, reasons, and executes everything end-to-end • Reality check: Monolithic agents become impossible to debug, optimize, or improve incrementally 3. Instant Business Output • Agent actions quickly translate into usable results • Reality check: Without validation, "instant" often means "instantly wrong" What Actually Scales (Reality) 1. Task Decomposition • Breaking goals into bounded, retry-safe execution steps • Why it matters: Complex workflows fail. Decomposed workflows can recover gracefully. 2. State & Memory Management • Persisting context, progress, and intermediate decisions • Why it matters: Stateless agents restart from zero on every failure. Stateful agents resume. 3. Workflow Orchestration • Controlled routing between planning, execution, and validation • Why it matters: Agents need traffic control, not just intelligence. Wrong sequence = wasted compute. 4. Tool Failure Handling • Retries, fallbacks, timeouts, and response validation • Why it matters: APIs fail. Networks timeout. Your agent needs to handle this without human intervention. 5. Context Budgeting • Managing tokens via retrieval, summaries, and pruning • Why it matters: Infinite context doesn't exist. You'll hit limits plan for it. 6. Guardrails & Controls • Preventing unsafe actions, loops, and unintended side effects • Why it matters: Autonomous agents without guardrails become liability generators. 7. Evaluation Loops • Measuring correctness, cost, and task completion quality • Why it matters: You can't improve what you don't measure. Production agents need continuous assessment. 8. Observability & Tracing • Tracking decisions, tool usage, latency, and failures • Why it matters: "The agent did not work" is not debuggable. Full traces are. What teams underestimate: The engineering effort is not in getting the agent to work once it is in making it work reliably 10,000 times across edge cases you did not anticipate. My recommendation: Design for the reality architecture from day one, even if you implement it incrementally. Your first version can skip advanced orchestration, but the hooks for state management and observability should be there. Starting simple is fine. Starting without a plan for complexity is expensive. ♻️ Repost this to help your network get started ➕ Follow Sivasankar for more

Process chaos isn’t just frustrating. It’s destroying your profit margins. I saw this in action yesterday: a nail appointment turned into a 2-hour productivity nightmare. 💅 Not because they were busy. Not because they were short-staffed. But because of process blindness. The scene was painfully familiar: no appointment system, constant interruptions, staff juggling too much, and frustrated customers. If this sounds like your business, you’re leaving money on the table. Research shows automation can free up 20–30% of managers’ time and improve accuracy and efficiency across the board. Throwing more hours or people at process problems doesn’t solve them. You need intelligent systems to cut through the noise. Here are 7 automation solutions we implement in our Culture & Workflow Reset program, with simple action steps: 1️⃣ Client Communication Hub AI phone systems handle calls and bookings automatically. ⏱ Cuts interruptions, saves 3–5 hours per week per employee. 👉 Replace your front-desk phone with an AI-enabled system that auto-books into your calendar and routes urgent calls only. 2️⃣ Automated Client Experience Smart follow-ups, confirmations, and reminders. 📈 Reduces no-shows by up to 29% and boosts client satisfaction. 👉Use an AI CRM that sends automated confirmations, follow-ups, and post-appointment surveys without staff time. 3️⃣ Intelligent Task Management AI assigns and prioritizes work. ⚡ Cuts management overhead by 25–30% and reduces delays. 👉 Integrate tools like Asana, ClickUp, or Monday.com with AI rules so recurring tasks are auto-assigned to the right person. 4️⃣ Process Documentation Auto-generated SOPs and training guides. 📘 Speeds onboarding by 40% and reduces early mistakes. 👉 Use AI transcription and process mapping tools like Scribe or Loom to automatically turn workflows into step-by-step guides. 5️⃣ Real-Time Customer Analytics AI feedback and trend tracking. 🔍 Issues identified 2x faster, with 75% more accurate resolutions. 👉 Add AI-powered survey tools like Qualtrics or Medallia that analyze responses instantly and flag emerging issues. 6️⃣ Admin Automation Smart invoicing, reporting, and data entry. 💰 Saves 8–10 hours per month per employee, with more than 90% accuracy. 👉 Connect your finance system to AI-powered invoicing like QuickBooks, Xero, or Bill.com so invoices and reports run automatically. 7️⃣ Dynamic Resource Planning AI-optimized scheduling and resource allocation. 📊 Improves utilization by 20% and reduces overtime costs by 25–30%. 👉 Use AI scheduling tools that balance workload across staff, auto-adjust when demand shifts, and prevent double-bookings. Ready to stop losing time and money to process chaos? Comment RESET or DM me to book your 30-minute Workflow Assessment. ♻️ Share if your company needs a culture reset ➕ Follow Rene Madden for more insights on driving transformation in financial services

Exploring Autonomous Agents: Why They Fail at Tasks Autonomous agents powered by LLMs promise to automate complex tasks — from data analysis to web crawling. But how well do they actually perform? A recent study evaluated 34 programmable tasks across three open-source frameworks (TaskWeaver, MetaGPT, AutoGen) with GPT-4o and GPT-4o-mini. ▪️▪️ Key Findings ▪️Agents completed only ~50% of tasks successfully. Failures often stemmed from: ▪️Planning errors – poor task decomposition, redundant steps, unrealistic plans. ▪️Execution issues – buggy code, API misuse, environment errors. Response generation mistakes – formatting errors, context loss, infinite loops. ▪️Surprisingly, the lighter GPT-4o-mini sometimes outperformed GPT-4o, especially in web crawling, where “overthinking” by the larger model caused failures. ▪️▪️Proposed Solutions ▪️Learning-from-feedback planning – agents must refine plans dynamically based on execution results. ▪️Early-stop & navigation mechanisms – detect loops, reroute tasks, and avoid wasted computation. ▪️▪️Takeaway: Today’s LLM-based agents show great promise but remain fragile. Robust planning, self-diagnosis, and adaptive error recovery will be crucial for moving from experimental demos to reliable, production-ready systems #AI #LLM #AutonomousAgents #MachineLearning #AIProductManagement #GenerativeAI

I've been running 23 automated AI tasks since January. They process my inbox at 6 AM. Prep my client meetings by 8 AM. Ping me on Telegram when something needs attention. Run health checks across my entire client portfolio. All on autopilot. While I sleep. Two months ago, I showed this system to my newsletter audience. The most common response was some version of: That's incredible. But I can't build that. Fair. My setup took months. Custom PowerShell scripts. Windows Task Scheduler configurations. MCP server integrations. A safe-execution wrapper to kill zombie processes. Not exactly beginner-friendly. This month, Claude shipped 16 product updates in 21 days. 4 of them just made my custom setup available to everyone. No scripts. No configurations. No technical background required. Here's what's now possible: Upload 3,000 pages into one conversation. A full OM, five years of financials, rent rolls, comps, and your underwriting assumptions. All at once. No splitting. No losing context. Schedule AI tasks to run on autopilot. Deal screening at 6 AM. Portfolio rent roll checks every Monday. Lease expiration alerts daily. Set it once, walk away. Control your AI from your phone. Start an analysis at your desk, redirect it from a property tour, get results in the parking lot. One continuous thread that never resets. Get texted when it's done. Your AI finishes a 200-page OM analysis at 2 AM? Telegram ping with the summary. No more checking back every 20 minutes. Claude didn't get smarter this month. It got easier. The technical barrier that separated Level 1 from Level 3 just disappeared. I wrote the full breakdown in this week's newsletter - what each feature does, how to set it up for CRE, and a copy-paste prompt for every use case.

I just built a new agent orchestration system for Claude Code: npx claude-flow, Deploy a full AI agent coordination system in seconds! Launch a self-directed team of low-cost AI agents working in parallel. With claude-flow, I can spin up a full AI R&D team faster than I can brew coffee. One agent researches. Another implements. A third tests. A fourth deploys. They operate independently, yet they collaborate as if they’ve worked together for years. What makes this setup even more powerful is how cheap it is to scale. Using Claude Max or the Anthropic all-you-can-eat $20, $100, or $200 plans, I can run dozens of Claude-powered agents without worrying about token costs. It’s efficient, persistent, and cost-predictable. For what you'd pay a junior dev for a few hours, you can operate an entire autonomous engineering team all month long. The real breakthrough came when I realized I could use claude-flow to build claude-flow. Recursive development in action. I created a smart orchestration layer with tasking, monitoring, memory, and coordination, all powered by the same agents it manages. It’s self-replicating, self-improving, and completely modular. This is what agentic engineering should look like: autonomous, coordinated, persistent, and endlessly scalable. Technical architecture at a glance - Orchestrator: Assigns tasks, monitors agents, and maintains system state - Memory Bank: CRDT-powered, Markdown-readable, SQLite-backed shared knowledge - Terminal Manager: Manages shell sessions with pooling, recycling, and VSCode integration - Task Scheduler: Prioritized queues with dependency tracking and automatic retry - MCP Server: Stdio and HTTP support for seamless tool integration

Please, stop building AI agents. If you want to succeed as an AI engineer, you need to get this: Building autonomous agents isn’t just about prompting clever instructions. It’s an operational marathon. And if you’re not thinking in LLMOps layers, Your agents will break at scale. Here’s the Ops Blueprint you need: Core LLMOps Layers: The Foundation 1. The Data Layer This is the bedrock of LLM systems. Every retrieval, Every response, Every plan your agent makes Relies on the quality of your data. From sourcing → cleaning → chunking → embedding → indexing This pipeline determines how relevant and accurate your context is. Garbage in, garbage out is a 𝘭𝘢𝘸. 2. Prompt Lifecycle Management Prompts aren’t static text; They’re evolving artefacts. You need registries, versioning systems, and testing workflows to operationalise prompt changes. Treat them as code: modular, testable, and traceable. 3. Model Specialisation & Serving Fine-tuning isn't always required, but when it is, you need pipelines that can train on your domain-specific data while managing drift and cost. More broadly, this layer covers how your models (fine-tuned or base) are deployed, scaled, and served efficiently via inference endpoints, often with caching and routing logic. 4. Monitoring & Guardrails The mission control layer. Track usage, latency, token consumption, hallucinations, and behavioural anomalies. Integrate feedback loops to continuously improve both performance and safety. The Agentic Upgrade: Advanced Ops for Autonomy 1. Agent Orchestration & Tooling Autonomous agents are planners and doers. This layer governs how they decompose goals, sequence actions, and interact with external tools and APIs to complete tasks. 2. State & Memory Management Agents need 𝘴𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘴𝘵𝘢𝘵𝘦 (e.g. current task status) and 𝘭𝘰𝘯𝘨-𝘵𝘦𝘳𝘮 𝘮𝘦𝘮𝘰𝘳𝘺(e.g. persistent knowledge or preferences). Operationalising this involves orchestrating the entire memory layer. 3. Multi-Layer Evaluation You can’t rely on pass/fail metrics anymore. Agents need evaluation across reasoning chains, tool usage, response coherence, and task success rates. Operationalising evaluation = continuous QA pipelines + human-in-the-loop reviews + synthetic testing. Resources to get you started: Data Engineering: • https://lnkd.in/g9RyKh4P • https://lnkd.in/ggfHUGet • https://lnkd.in/ghUWTxc7 LLMOps: • https://lnkd.in/gkxqUAVs • https://lnkd.in/gfWhes7d • https://lnkd.in/gS_tA8hJ Prompt Lifecycle: • https://lnkd.in/g2RmYfKh LLM Observability: • https://lnkd.in/gHyEpgBJ ♻️ Reposting this helps everyone in your network upskill

Claude Code is only as good as your orchestration workflow. I spent 6 months testing it so you don't have to. [ P.S. You can get my Ultimate Claude Code guide for engineers here at no cost: https://lnkd.in/e64Jvdrt ] So Boris Cherny (the creator of Claude Code at Anthropic) recently shared the internal best practices his team actually uses daily. Someone brilliantly distilled those threads into a structured CLAUDE .md file you can drop straight into any project root. It acts as a system prompt. Turns Claude into a far more autonomous, rigorous engineering partner. Here's what it does: → 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Mandates "Plan Node Default" for any task over 3 steps. Uses subagents liberally to keep the main context window clean. → 𝗦𝗲𝗹𝗳-𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗟𝗼𝗼𝗽: This is the real magic. After ANY correction, it updates a tasks/lessons .md file. You're building a compounding system where the mistake rate drops over time because it actively learns from your feedback. → 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗗𝗼𝗻𝗲: Can't mark a task complete without proving it works. Diffs behavior. Runs tests. Checks logs. The bar? "Would a staff engineer approve this?" → 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗕𝘂𝗴 𝗙𝗶𝘅𝗶𝗻𝗴: Zero hand-holding. Point it at failing CI tests or error logs... it just goes to work. No constant context switching from you. → 𝗦𝘁𝗿𝗶𝗰𝘁 𝗧𝗮𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Forces a "Plan First" approach written to a todo .md with checkable items before any implementation starts. And perhaps the most important part? It forces the AI to prioritize simplicity, find root causes instead of temporary fixes, and minimize the blast radius of every change. Senior developer standards. Not shortcuts. If you're spending hours a day in the terminal with AI, setting up a strong .md instruction file like this isn't optional anymore. It's the difference between AI that drifts and AI that compounds. It takes time to set up. But if you do, you're ahead of almost everyone else.