Optimizing Workflow Processes

“What AI skill should my team and I actually learn right now?” I will scream this from the rooftops of NYC. ➡️ Learn agent delegation Target a dedicated workflow or task. Assign an AI agent said role, define the outcome, set constraints, and schedule review gates. Treat it like a junior teammate and give it work, while monitoring so you can review for accuracy. Here’s my do-this-now stack, and how I’d run it with a team ⏬ If you’re a beginner: Start with ChatGPT Agent Mode. Open a new ChatGPT chat and change the dropdown to ‘Agent Mode’. It can plan tasks, execute steps, and return cited outputs for market scans, vendor comparisons, executive briefs, and decision memos. Kick off the job, let it run, WATCH IT RUN, and then review the completion. If you’re more technical or ops-heavy: Use Claude Code when the work requires operating UIs or your computer - clicking through portals, filling forms, wrangling spreadsheets, saving down documents. Expect more upfront setup and ownership, so keep a step-by-step prompt checklist, add automatic reruns for failing steps, and update the checklist only when the site’s labels or paths change. If you’re living in Google Workspace: Turn on Google connectors (Drive, Gmail, Calendar) inside ChatGPT or Claude. Ask the model to find your team’s file, summarize threads, compare document versions, prepare for and schedule meetings, or draft from past emails. This lets your agent pull context and act on it without manual hunting. How to turn this into outcomes in 30 days ⏬ → Twice a week, use Agent Mode to produce a one-page brief with citations and a recommendation on a real business question. Track cycle time and data/citation quality, and, where relevant, use Claude Code to automate in parallel. At the end of the month, you should know where a few agents can tackle real work and have the data to support what to scale. #AIinWork

It’s funny, but this harsh reality also highlights a serious truth: AI is powerful, but it’s not infallible. Algorithms can misinterpret context, miss nuance, or make mistakes that a human would never make. Blind trust can be dangerous, whether you’re eating a mushroom or making business decisions. So how can we question AI outputs and make better decisions? Here are a few strategies I use: Check the source – Where did the AI get its data? Is it reliable, up-to-date, and relevant to your situation? Cross-verify – Don’t take a single answer at face value. Look for supporting evidence or alternative perspectives. Consider context – AI can miss nuances that matter. Ask: “Does this recommendation make sense given my goals, constraints, and values?” Ask why, not just what – Probe AI suggestions: “Why is this solution recommended?” Understanding reasoning helps spot gaps. Add human oversight – Involve experts, mentors, or peers to validate outputs before acting. AI is a powerful partner, but decisions should still be human-led. Our judgment, skepticism, and experience are what turn insights into smart action. 💬 How do you validate AI recommendations in your work to avoid costly mistakes? #AI #CriticalThinking #Leadership #FutureOfWork #LearningAndDevelopment #TrustButVerify

At Amazon, I’ve built pipelines that move thousands of gigabytes of data. At Amazon, I’ve also built platforms used by hundreds of teams across the organization. But do you know how I got the opportunity to do these things? → It was because of one simple mindset shift: I stopped thinking like a pipeline builder. And started thinking like a product builder. Here’s what that shift looks like in real life 👇 1. Optimize for adoption, not just execution A fast Spark job is nice. But a pipeline that any team can deploy, monitor, and debug without you? That’s a game-changer. If your internal users are struggling, that’s a UX bug. 2. Design APIs, not one-off scripts Your Airflow DAGs and Glue jobs should feel like APIs. Versioned, observable, with clear inputs/outputs. That’s how you build trust at scale. 3. Surface friction like a PM If people keep pinging you for creds, schemas, or weird Athena errors, that’s a signal. Treat those moments like product bugs. Fix them once, and fix them for everyone. 4. Metrics = feedback loops In product, you track conversion. In data platforms, track usage: → How many teams use your tools? → How often do they fail? → Who’s stuck? These are your feature requests. 5. Think enablement > control Great platforms don’t block, they enable. Guardrails should guide, not restrict. Make it easy to do the right thing. I’ve learned this the hard way. When you think like a product builder, your work scales. It doesn’t stop at you. It becomes a system that helps others move faster. So next time you're building a data pipeline, ask yourself: What would this look like if it were a product? Let’s build platforms that people actually want to use.

When working with multiple LLM providers, managing prompts, and handling complex data flows — structure isn't a luxury, it's a necessity. A well-organized architecture enables: → Collaboration between ML engineers and developers → Rapid experimentation with reproducibility → Consistent error handling, rate limiting, and logging → Clear separation of configuration (YAML) and logic (code) 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗧𝗵𝗮𝘁 𝗗𝗿𝗶𝘃𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 It’s not just about folder layout — it’s how components interact and scale together: → Centralized configuration using YAML files → A dedicated prompt engineering module with templates and few-shot examples → Properly sandboxed model clients with standardized interfaces → Utilities for caching, observability, and structured logging → Modular handlers for managing API calls and workflows This setup can save teams countless hours in debugging, onboarding, and scaling real-world GenAI systems — whether you're building RAG pipelines, fine-tuning models, or developing agent-based architectures. → What’s your go-to project structure when working with LLMs or Generative AI systems? Let’s share ideas and learn from each other.

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

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?

Blindly Trusting Vendor Data Is a Costly Engineering Mistake Blindly trusting vendor data is one of the most common—and most expensive—mistakes in process engineering. Vendor datasheets are not wrong, but they are not automatically right for your process. As process engineers, we often receive neatly prepared datasheets showing: → Guaranteed performance → High efficiencies → Compliance with standards But here’s the uncomfortable truth 👇 Most equipment failures don’t happen because vendors lied. They happen because engineers stopped questioning. ⚠️ Where Blind Trust Goes Wrong → Rated flow assumed as operating flow → Normal case considered, part-load ignored → Turndown and minimum flow not verified → Fouling, aging, and degradation overlooked → Utilities and site limitations not cross-checked A pump that works perfectly on paper can cavitate in the plant. A heat exchanger that meets duty can fail after six months. A control valve sized “as per datasheet” can generate noise and vibration. 🧠 The Real Engineering Mindset Vendors design equipment. Process engineers design systems. Your responsibility is not to approve numbers. Your responsibility is to protect plant operability and reliability. Always ask: → What is the design basis? → What are the operating and off-design cases? → What happens at minimum flow or maximum turndown? → What will change after two years of operation? ✅ Remember This Vendor data is an input, not a conclusion. Verification is engineering. Blind trust is assumption. If you want to grow as a process engineer, challenge the data—before the plant challenges you. #ProcessEngineering #ProcessDesign #ChemicalEngineering #EPCProjects #PlantDesign #EngineeringReality #ProcessEngineer #MyProcessDesign #ProcessEngineering #ChemicalEngineering #ProcessDesign #Engineering #EngineeringLife #EPC #EPCProjects #PlantDesign #OilAndGas #Refinery #Petrochemical #ProcessEngineer #PlantEngineering #DesignEngineering #EquipmentDesign #EngineeringReality #EngineeringCareer #LearningByDoing #ProfessionalGrowth #EngineeringMindset #MyProcessDesign #EngineeringInsights #ProcessDesignEngineering

This is a visual representation of why your team hates Salesforce 😡... Throughout my Salesforce journey, I've seen it all (Insert "Emotional Damage" meme 🫠). One common issue I see often are Flows that "work," but that are not optimized for scale or user experience. They cause ugly error messages, delays on future iteration, & inaccurate data that plague users on a daily basis. Check out the Flow examples below: Version 1 works. It's simple, has only 2 elements, so what's the big deal? To find out, let's look at the #'d boxes in Version 2: 1️⃣ Element Descriptions: Please...for the love of Benioff... document the "Why." Each element allows you to write a description, which explains what it's doing technically and why it's important to the process you're building. This context is essential for future changes and for those that come after you. If another admin can't read your descriptions and understand what it's doing, you haven't documented enough! 2️⃣ Decision Elements after Get Records Elements: In Version 1, the "Get Account Id" element finds a related Account record associated with the triggering Opportunity. What happens if the criteria for the search doesn't find a record? ❌ Flow Error ❌. By checking to see if the Get Records element finds what it's looking for, you can prevent a poor user experience and ensure other automation runs on schedule. 3️⃣ Fault Paths & Error Handling: A fault path is an error handling path that triggers when the element wasn't able to process a change (Update, Create, Delete) in the database. By default, users are presented with red text and a cryptic message without enough readable context to troubleshoot themselves. In Version 2, we've add a fault path for every Create Records element to notify the Salesforce team of new errors. No one likes it when automation fails, but it's a magical experience to reach out to a user and let them know you're already working on it! 🪄🎩🐇 4️⃣ Tracking Performance/Usability: This one is a game changer... What good is an active Flow if you can't measure its performance or usability? Create a custom object called "Automation Saved Time." Any time you add to a Flow, estimate the amount of time the automation saves and add it to a variable. At the end of the Flow, create a new Automation Saved Time record adding the aggregated time for all elements. It'll help answer some amazing questions: a) How much time has your Flow saved users? b) How often has Flow is been run? c) Is this Flow useful? All questions you can only assume the answers to without this data! Build a dashboard and show it to internal stakeholders, so they understand the value you're adding. 5️⃣ Reuse & Recycle: Rather than building a new Flow element each time you need it, connect to an existing element. In this example, we are connecting both fault paths to the same email alert. "In a world full of Version 1s, be a Version 2 💪🏻" #salesforce #salesforceflow #automation #bestpractices #benioff

If you’re building anything with LLMs, your system architecture matters more than your prompts. Most people stop at “call the model, get the output.” But LLM-native systems need workflows, blueprints that define how multiple LLM calls interact, how routing, evaluation, memory, tools, or chaining come into play. Here’s a breakdown of 6 core LLM workflows I see in production: 🧠 LLM Augmentation Classic RAG + tools setup. The model augments its own capabilities using: → Retrieval (e.g., from vector DBs) → Tool use (e.g., calculators, APIs) → Memory (short-term or long-term context) 🔗 Prompt Chaining Workflow Sequential reasoning across steps. Each output is validated (pass/fail) → passed to the next model. Great for multi-stage tasks like reasoning, summarizing, translating, and evaluating. 🛣 LLM Routing Workflow Input routed to different models (or prompts) based on the type of task. Example: classification → Q&A → summarization all handled by different call paths. 📊 LLM Parallelization Workflow (Aggregator) Run multiple models/tasks in parallel → aggregate the outputs. Useful for ensembling or sourcing multiple perspectives. 🎼 LLM Parallelization Workflow (Synthesizer) A more orchestrated version with a control layer. Think: multi-agent systems with a conductor + synthesizer to harmonize responses. 🧪 Evaluator–Optimizer Workflow The most underrated architecture. One LLM generates. Another evaluates (pass/fail + feedback). This loop continues until quality thresholds are met. If you’re an AI engineer, don’t just build for single-shot inference. Design workflows that scale, self-correct, and adapt. 📌 Save this visual for your next project architecture review. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Most people think the risk with AI is that it sometimes says something dumb. I think the bigger risk is the opposite: it says something clean, confident, and well-formatted… and it’s quietly wrong.   A recent Microsoft Research study stress-tested frontier models (including GPT-5) and ran a deceptively simple experiment: remove the image that a question depends on (like a chest X-ray) and see what happens. Models still answer above random chance. That doesn’t mean the model “saw” anything — it can often “pass” by leaning on shortcuts and priors.   Now swap “X-ray” with “the attachment,” “the pricing spreadsheet,” or “Section L.”   This is where a lot of AI-assisted RFP workflows break: upload the solicitation, run one prompt, and accept the generated outline/compliance matrix with no verification loop. The output looks professional. But without traceability (show me the exact source text) and a human correction loop, you can get an illusion of compliance — not real compliance.   To be clear, this is not an anti-LLM take. Models will keep improving. My point is architectural: reliability comes from workflow design, not blind faith in a single pass.   I wrote a deeper article outlining the predictable failure modes (misinterpretation, omission, and document artifacts) and what a safer workflow looks like: traceability, red-flags, and fix-in-place review.