Workflow Automation Solutions

How we saved 10+ hours weekly by giving finance a simple interface. Our finance team was processing invoices the same way for years: 1. Email attachments → 2. Manual download → 3. Print → 4. Physical signature → 5. Scan → 6. Manual data entry The entire cycle took 3-5 days. The request to "build a proper approval system" kept getting deprioritized—it felt like a multi-month project. We reframed the problem: We didn't need a complex system. We just needed to connect two things: the data from our accounting software's API and a simple list where the right people could click "Approve" or "Reject." What actually got built: • A single-page app that pulls unpaid invoices automatically • Logic that routes invoices over $5k to directors, others to managers • A comment field for rejections • A basic audit log showing who approved what and when What changed: ✅ Approvals now happen in under 24 hours ✅ The finance team stopped chasing paper trails ✅ Vendors get paid faster ✅ Every decision is logged automatically The takeaway: Sometimes "digital transformation" isn't about big platforms. It's about giving a team one less PDF to manage by building a simple, focused tool that sits on top of the data they already use. What's the most stubborn, repetitive task in your team's workflow? Often the highest-impact tools are the smallest ones that remove a single point of friction. https://uibakery.io/ #ProcessAutomation #FinanceTech #OperationalEfficiency #DigitalTransformation

Building multi-agent systems will be a "must have" in 2026. Here is how to obtain this skill: Learn n8n. The world's most popular low-code platform for building AI workflows. It lets you mix traditional business workflows with AI agents visually. 1,000+ integrations. No backend code required. Here's what every PM needs to know: 1. Orchestration beats agency Fully autonomous agents still fail 10%+ of the time. n8n lets you orchestrate the predictable parts and use AI only where you need it. Cheaper, faster, more reliable. Andrej Karpathy said it: the industry is "overshooting the tooling w.r.t. present capability." 2. Three ways to build AI workflows Take competitor research as an example: • LLM Workflow: fully orchestrated, zero autonomy. You control every step. • Agentic Workflow: orchestrated with detailed steps in the agent prompt. Low autonomy. • AI Agent: prompt contains only the objective. Agent picks its own tools. All three work reliably. For production, LLM workflows win. 3. The nodes that matter Triggers: schedule, webhook, app events, MCP server AI: "Message a model" for summarization, "AI Agent" for multi-step research Flow control: merge, filter, if/switch, loops, sub-workflows Core: JavaScript/Python code, HTTP requests, shell commands 4. Human in the loop For sensitive actions like updating pricing or publishing reports, route through Slack, Gmail, or Telegram for approval before executing. 5. Best practices most people miss • Pin data during development to skip expensive API calls • Set agent iterations to 20-30 (defaults are too low) • Use $execution.id for agent memory between iterations • Use $fromAI("paramName") to let agents define tool parameters • Build an error notification workflow from day one 6. Three ethical hacks for the free Community Edition • Remove the 1-day workflow history limit (set retention to 60 days via .yaml) • Use a Data Table as global variables • Auto-export all workflow definitions to GitHub for version control This is one of the highest ROI skills for building an AI PM portfolio. RAG chatbots, voice agents, multi-agent research systems. All without writing backend code. Swipe through the carousel for the full breakdown. Full guide by Paweł Huryn: https://lnkd.in/gBsXaHzy Video walkthrough: https://lnkd.in/gx7K4ZjV Pawel's AI PM Certificate: https://lnkd.in/eZ2uUySG Ship a multi-agent system this weekend. Your future self will thank you.

3 Workflows I've Automated for in-house teams. ① Ask Legal ② Procurement ③ Contract Review (not just the review!) 1. Ask Legal [or any department for that matter 🤷🏼♀️] You've heard me talk about legal teams and knowledge management. Long story short, your legal team is answering the same 20 questions over and over 😵💫 A simple way to save a CHUNK of time answering questions from the business (enabling them to go faster) ALL while having complete control & keeping a human in the loop? ↪️ Set up an 'Ask Legal' bot in your comms platform. ↪️ Sync it with your knowledge base (e.g GDrive/Notion/Sharepoint). ↪️ Set up your custom instructions (Want it to tag Bob on privacy questions only, specifically on a Tuesday? No problem).  ↪️ Don't want the answer to go straight out to the business without reviewing it first? Cool, turn on co-pilot mode. The result? 60-80% fewer repetitive queries. Your team focuses on the high value things that need a human lawyer. 2. Procurement Businesses have 100's of tools, but when departments don't speak to each other you end up with duplicate tools & subscriptions 😭 💵 🚽.  What if there was a way for the business to find out in <1 minute if there was a tool available that covered their needs, before needing to spend some hard secured department budget? Moreover, what if I told you, they could kick off the internal procurement process from the comfort of your comms platform? Team member : “Do we already have a tool for X?” in Slack/Teams ✅ Bot checks knowledge base (policies, procurement tool). ✅ If a match is found, it shares the approved tool & owner to contact. ✅ If not, the bot can ask the user for more info and direct them with next steps to kick off the procurement process from inside Slack/Teams. Ensuring your users ACTUALLY follow the process, without adding friction. Did I just see your CFO cry tears of joy? 3. Third Party Vendor Contract Review & Project Management Getting AI to redline a contract (as a first pass) is a huge win, but there's still the other pieces of the process missing, like: 🤷🏼♀️ The business figuring out IF legal review is even needed (according to company policy). 📨 The business actually submitting the contract to legal. 😩 Managing review capacity within the legal team. 🖥️ Getting the legal team to log & update the PM tool. The list never ends. Legal reviews only what actually needs their eyes, turnaround times improve, and the business stops pinging the team for “update pls?” in Slack : ) TLDR; Most legal teams are drowning in admin work that could be automated. I've built all of these using simple processes and tools (that I've found most businesses have). You also know I love a good Figma flow. So I’ve built them for all three of the above (see a sneak peak below). Want the entire thing? Comment "FLOWS" and I'll send them over. Also, tell me what you want to see - more of the above or step-by-step how-to build videos?

LlamaIndex just unveiled a new approach involving AI agents for reliable document processing, from processing invoices to insurance claims and contract reviews. LlamaIndex’s new architecture, Agentic Document Workflows (ADW), goes beyond basic retrieval and extraction to orchestrate end-to-end document processing and decision-making. Imagine a contract review workflow: you don't just parse terms, you identify potential risks, cross-reference regulations, and recommend compliance actions. This level of coordination requires an agentic framework that maintains context, applies business rules, and interacts with multiple system components. Here’s how ADW works at a high level: (1) Document parsing and structuring – using robust tools like LlamaParse to extract relevant fields from contracts, invoices, or medical records. (2) Stateful agents – coordinating each step of the process, maintaining context across multiple documents, and applying logic to generate actionable outputs. (3) Retrieval and reference – tapping into knowledge bases via LlamaCloud to cross-check policies, regulations, or best practices in real-time. (4) Actionable recommendations – delivering insights that help professionals make informed decisions rather than just handing over raw text. ADW provides a path to building truly “intelligent” document systems that augment rather than replace human expertise. From legal contract reviews to patient case summaries, invoice processing, and insurance claims management—ADW supports human decision-making with context-rich workflows rather than one-off extractions. Ready to use notebooks https://lnkd.in/gQbHTTWC More open-source tools for AI agent developers in my recent blog post https://lnkd.in/gCySSuS3

You must know these 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 as an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. If you are building Agentic Systems in an Enterprise setting you will soon discover that the simplest workflow patterns work the best and bring the most business value. At the end of last year Anthropic did a great job summarising the top patterns for these workflows and they still hold strong. Let’s explore what they are and where each can be useful: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗖𝗵𝗮𝗶𝗻𝗶𝗻𝗴: This pattern decomposes a complex task and tries to solve it in manageable pieces by chaining them together. Output of one LLM call becomes an output to another. ✅ In most cases such decomposition results in higher accuracy with sacrifice for latency. ℹ️ In heavy production use cases Prompt Chaining would be combined with following patterns, a pattern replace an LLM Call node in Prompt Chaining pattern. 𝟮. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: In this pattern, the input is classified into multiple potential paths and the appropriate is taken. ✅ Useful when the workflow is complex and specific topology paths could be more efficiently solved by a specialized workflow. ℹ️ Example: Agentic Chatbot - should I answer the question with RAG or should I perform some actions that a user has prompted for? 𝟯. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Initial input is split into multiple queries to be passed to the LLM, then the answers are aggregated to produce the final answer. ✅ Useful when speed is important and multiple inputs can be processed in parallel without needing to wait for other outputs. Also, when additional accuracy is required. ℹ️ Example 1: Query rewrite in Agentic RAG to produce multiple different queries for majority voting. Improves accuracy. ℹ️ Example 2: Multiple items are extracted from an invoice, all of them can be processed further in parallel for better speed. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: An orchestrator LLM dynamically breaks down tasks and delegates to other LLMs or sub-workflows. ✅ Useful when the system is complex and there is no clear hardcoded topology path to achieve the final result. ℹ️ Example: Choice of datasets to be used in Agentic RAG. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: Generator LLM produces a result then Evaluator LLM evaluates it and provides feedback for further improvement if necessary. ✅ Useful for tasks that require continuous refinement. ℹ️ Example: Deep Research Agent workflow when refinement of a report paragraph via continuous web search is required. 𝗧𝗶𝗽𝘀: ❗️ Before going for full fledged Agents you should always try to solve a problem with simpler Workflows described in the article. What are the most complex workflows you have deployed to production? Let me know in the comments 👇

LLMOps in the Real World ⬇️ 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? There is plenty of advice on how to build agent prototypes that > use third-party API, like OpenAI or Antrhopic. > encapsulate all the agent + tooling logic inside a single Python program > run locally with docker compose But the thing is, companies out there need WAY MORE than this to extract actual business value from this technology. To scale this prototypes into fully working systems, without breaking the bank, you need to use the right infrastructure and tooling. This is what LLMOps in the Real World are. And this is what Marius Rugan and myself will start teaching from today. 𝗦𝘆𝘀𝘁𝗲𝗺 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 📐 Before we get our hands-dirty with specific tools and components, we need to understand the backbone and system architecture. Agentic workflows are way more than a Python program. They are a collection of services, running inside a Kubernetes cluster. (We we will cover "Why Kubernetes?" in our first video some time next week. Bear with me for a second here) These services are: > 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗳𝗸𝗹𝗼𝘄 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻𝘀, typically written in Python using a library like Langgraph or Crew AI, or even better in Rust. > 𝗟𝗟𝗠 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 running on GPU nodes, that serve the text completions the agent workflows need for reasoning, sumarization, tool parameter parsing... > 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 (MCP servers) and clients that connect agents to the internal services of your company (aka the tools), which can be - read-only, for example a data warehouse in PostgreSQL, or - read-and-write, for example the WhatsappAPI to send and receive customer messages. To understand the system is working the way you expect it to work, you need to collect and visualize logs and metrics, from all these services, using battle tested tooling like Prometheus Group, Grafana Labs and the new kid on the block Comet's Opik. 𝗔𝗴𝗲𝗻𝘁𝟮𝗔𝗴𝗲𝗻𝘁 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 🔮 As the number of agents increase, you can get extra value by enabling collaboration between them, using the newly released Agent2Agent protocol by Google. The future is exciting. Let's get there one step at a time. Wanna learn how to build systems like this from scratch? In the next weeks Marius Rugan will start the first series on LLMOps in the Real World. In public. For FREE. Follow Pau Labarta Bajo and Marius Rugan so you don't miss what is coming next.

Your best strategist spent yesterday afternoon resetting broken mailboxes instead of building campaigns. That’s the hidden cost of scaling outbound that nobody talks about. The more clients you take on, the more your best people end up doing technical cleanup work instead of strategy. Fixing inboxes. Rebuilding domains. Checking deliverability. Resetting what should already just work. I got tired of watching high-value hours disappear into backend maintenance. So I moved the whole setup behind Claude + Maildoso MCP. Now I just describe what I want, and the system executes it. 1. Set up the infrastructure Maildoso creates cold email domains and inboxes. SPF, DKIM, and DMARC are handled automatically. Your main domain stays safe. 2. Connect it to Claude Add your API key. Plug Maildoso MCP into Claude Desktop. Now Claude can directly run your email infrastructure. 3. Tell Claude what to build Example: “Create 5 domains, set up 3 inboxes per domain, add a reply forwarding inbox, and check deliverability so only healthy inboxes are used.” Claude runs the setup through the API. The real win is monitoring. Maildoso keeps checking inbox health every few days and pauses bad ones before they hurt your campaigns. So your team stops babysitting infrastructure. And goes back to doing actual strategy and campaigns. If your team is still manually managing inboxes, you’re burning your best talent on the wrong work. Over to you: where does outbound usually break for you, data quality or sending infrastructure? #Outbound #ColdEmail #SalesAutomation #Deliverability #AItools

I started by asking AI to do everything. Six months later, 65% of my agent’s workflow nodes run as non-AI code. The first version was fully agentic : every task went to an LLM. LLMs would confidently progress through tasks, though not always accurately. So I added tools to constrain what the LLM could call. Limited its ability to deviate. I added a Discovery tool to help the AI find those tools. Better, but not enough. Then I found Stripe’s minion architecture. Their insight : deterministic code handles the predictable ; LLMs tackle the ambiguous. I implemented blueprints, workflow charts written in code. Each blueprint specifies nodes, transitions between them, trigger conditions for matching tasks, & explicit error handling. This differs from skills or prompts. A skill tells the LLM what to do. A blueprint tells the system when to involve the LLM at all. Each blueprint is a directed graph of nodes. Nodes come in two types : deterministic (code) & agentic (LLM). Transitions between nodes can branch based on conditions. Deal pipeline updates, chat messages, & email routing account for 29% of workflows, all without a single LLM call. Company research, newsletter processing, & person research need the LLM for extraction & synthesis only. Another 36%. The workflow runs 67-91% as code. The LLM sees only what it needs : a chunk of text to summarize, a list to categorize, processed in one to three turns with constrained tools. Blog posts, document analysis, bug fixes are genuinely hybrid. 21% of workflows. Multiple LLM calls iterate toward quality. Only 14% remain fully agentic. Data transforms & error investigations. These tend to be coding tasks rather than evaluating a decision point in a workflow. The LLM needs freedom to explore. AI started doing everything. Now it handles routing, exceptions, research, planning, & coding. The rest runs without it. Is AI doing less? Yes. Is the system doing more? Also yes. The blueprints, the tools, the skills might be temporary scaffolding. With each new model release, capabilities expand. Tasks that required deterministic code six months ago might not tomorrow.

Most lawyers are using AI to do faster what they were already doing. That is not the opportunity. AI agents can complete entire workflows autonomously. Here are three examples from legal work: 1) Lease abstraction at scale. An agent reviews 400 commercial leases, extracts rent escalation clauses, flags deviations from the negotiated form, and outputs a variance report. Before a human opens a single document. What took a team two weeks now takes two hours. 2) Regulatory change management. Instead of a paralegal manually checking state-by-state privacy law updates, an agent monitors legislative feeds, maps changes to existing data processing agreements, and drafts a memo flagging the ones that require action. 3) Deal room diligence. In an M&A transaction, an agent ingests the virtual data room, surfaces missing representations, identifies indemnification gaps, and cross-references disclosed litigation against public court records. Autonomously. What all three share: a human sets the objective, verifies the data, and reviews the output. The agent handles every step in between. The professional responsibility question this raises is not whether to use these tools. It is how to structure meaningful supervision when you are reviewing an agent's work product rather than directing it step by step. That is the question lawyers need to be asking right now. I'm Colin, General Counsel at Malbek and author of The Legal Tech Ecosystem. #legaltech #contracts #law #business #learning

LLMs struggle with rationality in complex game theory situations, which are very common in the real world. However integrating structured game theory workflows into LLMs enables them to compute and execute optimal strategies such as Nash Equilibria. This will be vital for bringing AI into real-world situations, especially with the rise of agentic AI. The paper "Game-theoretic LLM: Agent Workflow for Negotiation Games" (link in comments) examines the performance of LLMs in strategic games and how to improve them. Highlights from the paper: 💡 Strategic Limitations of LLMs in Game Theory: LLMs struggle with rationality in complex game scenarios, particularly as game complexity increases. Despite their ability to process large amounts of data, LLMs often deviate from Nash Equilibria in games with larger payoff matrices or sequential decision trees. This limitation suggests a need for structured guidance to improve their strategic reasoning capabilities. 🔄 Workflow-Driven Rationality Improvements: Integrating game-theoretic workflows significantly enhances the performance of LLMs in strategic games. By guiding decision-making with principles like Nash Equilibria, Pareto optimality, and backward induction, LLMs showed improved ability to identify optimal strategies and robust rationality even in negotiation scenarios. 🤝 Negotiation as a Double-Edged Sword: Negotiations improved outcomes in coordination games but sometimes led LLMs away from Nash Equilibria in scenarios where these equilibria were not Pareto optimal. This reflects a tendency for LLMs to prioritize fairness or trust over strict game-theoretic rationality when engaging in dialogue with other agents. 🌐 Challenges with Incomplete Information: In incomplete-information games, LLMs demonstrated difficulty handling private valuations and uncertainty. Novel workflows incorporating Bayesian belief updating allowed agents to reason under uncertainty and propose envy-free, Pareto-optimal allocations. However, these scenarios highlighted the need for more nuanced algorithms to account for real-world negotiation dynamics. 📊 Model Variance in Performance: Different LLM models displayed varying levels of rationality and susceptibility to negotiation-induced deviations. For instance, model o1 consistently adhered more closely to Nash Equilibria compared to others, underscoring the importance of model-specific optimization for strategic tasks. 🚀 Practical Implications: The findings suggest LLMs can be optimized for strategic applications like automated negotiation, economic modeling, and collaborative problem-solving. However, careful design of workflows and prompts is essential to mitigate their inherent biases and enhance their utility in high-stakes, interactive environments.