Optimizing Workflow Processes

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

The real challenge in AI today isn’t just building an agent—it’s scaling it reliably in production. An AI agent that works in a demo often breaks when handling large, real-world workloads. Why? Because scaling requires a layered architecture with multiple interdependent components. Here’s a breakdown of the 8 essential building blocks for scalable AI agents: 𝟭. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Frameworks like LangGraph (scalable task graphs), CrewAI (role-based agents), and Autogen (multi-agent workflows) provide the backbone for orchestrating complex tasks. ADK and LlamaIndex help stitch together knowledge and actions. 𝟮. 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Agents don’t operate in isolation. They must plug into the real world:  • Third-party APIs for search, code, databases.  • OpenAI Functions & Tool Calling for structured execution.  • MCP (Model Context Protocol) for chaining tools consistently. 𝟯. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Memory is what turns a chatbot into an evolving agent.  • Short-term memory: Zep, MemGPT.  • Long-term memory: Vector DBs (Pinecone, Weaviate), Letta.  • Hybrid memory: Combined recall + contextual reasoning.  • This ensures agents “remember” past interactions while scaling across sessions. 𝟰. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Raw LLM outputs aren’t enough. Reasoning structures enable planning and self-correction:  • ReAct (reason + act)  • Reflexion (self-feedback)  • Plan-and-Solve / Tree of Thought These frameworks help agents adapt to dynamic tasks instead of producing static responses. 𝟱. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 Scalable agents need a grounding knowledge system:  • Vector DBs: Pinecone, Weaviate.  • Knowledge Graphs: Neo4j.  • Hybrid search models that blend semantic retrieval with structured reasoning. 𝟲. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲 This is the “operations layer” of an agent:  • Task control, retries, async ops.  • Latency optimization and parallel execution.  • Scaling and monitoring with platforms like Helicone. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 No enterprise system is complete without observability:  • Langfuse, Helicone for token tracking, error monitoring, and usage analytics.  • Permissions, filters, and compliance to meet enterprise-grade requirements. 𝟴. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 Agents must meet users where they work:  • Interfaces: Chat UI, Slack, dashboards.  • Cloud-native deployment: Docker + Kubernetes for resilience and scalability. Takeaway: Scaling AI agents is not about picking the “best LLM.” It’s about assembling the right stack of frameworks, memory, governance, and deployment pipelines—each acting as a building block in a larger system. As enterprises adopt agentic AI, the winners will be those who build with scalability in mind from day one. Question for you: When you think about scaling AI agents in your org, which area feels like the hardest gap—Memory Systems, Governance, or Execution Engines?

I just taught Claude to directly query my CRM. Complex workflows became single prompts: A month ago my network kept talking about something called Model Context Protocol (MCP). Initially abstract, I understood it simply as: MCP lets AI models directly access your existing tools and databases. Think of it like the invention of USB: → Before USB: Multiple incompatible ports → After USB: One universal connection → Before MCP: Custom data integrations → After MCP: Universal plug-and-play AI connectivity Then a week ago I got an email from my personal CRM provider Clay that they had support for MCP. Historically, CRMs have acted as passive databases, requiring manual interactions to deliver insights. Here is what I used to do when I wanted to know who within my network had changed roles recently: OLD PROCESS: → Log into Clay CRM, export contacts as CSV → Clean and format data in a spreadsheet → Copy-paste formatted data into Claude → Manually instruct Claude to analyze job changes → Copy Claude’s insights back to Clay → Update contact records individually → Manually set follow-up tasks for each contact NEW PROCESS: → Simply instruct Claude: “Identify contacts in my network who recently changed jobs, showing their old and new positions and when I last interacted with them.” → Claude directly accesses Clay via MCP → Finds contacts who’ve recently changed jobs → Instantly provides a detailed, actionable list The results aren't perfect, but they turned a previously tedious process into an effortless query. The technical setup took 5 minutes: → Generated a Clay API key → Connected through Clay’s Smithery page → Installed Node.js locally → Ran one terminal command → Restarted Claude, confirming integration MCP's power comes from three shifts: → From isolated silos to interconnected intelligence → From sequential tasks to seamless orchestration → From human middleware to direct and automated interactions While it is early days, I believe we are scratching the surface with what is possible. I'm now working with several of our portfolio companies to explore how we can do deeper AI integrations. In an age where everyone has access to similar AI tools, the real competitive advantage isn't the tool itself. It's how deeply you embed it into your workflows.

"You don’t need more leads. You need the right leads." (That's how we helped a SaaS founder double their qualified leads in just 60 days.) Just a few weeks ago, they were stuck: – Talking to people who couldn’t afford them – Wasting hours on dead-end calls – Feeling like their offer was "bad" (it wasn’t) Here’s what we did instead: 1. Defined their dream client (finally) No more guessing who they were selling to. 2. Rebuilt their outreach from scratch Every DM felt personal, not desperate. 3. Positioned their offer with clarity and urgency Their dream clients immediately got it. 4. Focused on fewer but better conversations Less noise. More "Where do I sign?" moments. 5. Said 'No' to wrong-fit leads (Yes, you have to say NO sometimes.) Result? – Higher quality conversations – Shorter sales cycles – Better clients – 2X qualified leads If you’re struggling with lead quality... Maybe it’s time to stop asking "How do I get more?" and start asking "How do I get better?” What’s ONE thing you’ll improve about your lead generation this month? (Drop it in comments.)

As a junior lawyer, I was told to "take ownership" of the work but didn't get much guidance on what it actually meant. Here are 6 actions that junior lawyers can take to do this: 1️⃣ Correspondence When you're copied into email chains with clients, offer to do the first draft of the document / task / email response instead of waiting for it to be delegated to you. You can also ghostwrite draft emails* (from instructing lawyer to client), instead of sending internal emails (you to instructing lawyer) that they'd need to redraft for the client. Include notes where you have questions / assumptions. 2️⃣ Project management Keep track of key dates and the next actions that would follow your immediate task and check if you can help out with those next actions too. If you're not sure, just ask! "Thanks for getting me to help out with this task. I'd love to stay involved in the matter but I haven't worked on this sort of project before. Would you mind walking me through the next steps and where somebody with my experience could assist?" Also, if you're waiting for a senior lawyer to review your work and it's approaching a deadline, give them a reminder. "Hi, just wanted to remind you that we need to send out that advice on Friday. I sent you a draft on Tuesday. Please let me know if there's anything I should amend, or if you'd like me to send it again." 3️⃣ Provide solutions, not problems If you identify a problem with a task / matter, take some time to think about a potential solution instead of just passing the problem to your supervisor. "As I was [doing this task], I found that [Step 2] wasn't working because of [reason]. I think we can still achieve the same result if we do [potential solution]. Do you think that would work?" 4️⃣ Be prepared to challenge instructions The ultimate goal is to achieve the client's desired outcome, not to perfectly follow instructions that may be flawed. If you see an issue with your instructing lawyer's (or the client's) instructions, speak up and be prepared to offer an alternative. 5️⃣ Understand the business side of things Doing the actual work isn't the whole job. Take some time to learn about the budget for the matters, your hourly rates, what to write in your billing narratives, how each client likes to communicate (phone calls, emails, client portal uploads), and who the client needs to report to / get approval from. 6️⃣ Communicate leave and coordinate handovers When you take leave – communicate in advance, check with your team to see if there is coverage, and give your team proper handovers for matters that might continue during your leave period. Sending a calendar invite for your leave period and preparing handover notes* can also be helpful. * I've written guides on the asterisked things. Let me know in the comments if you'd like a link. 📌What else do you think junior lawyers can do to demonstrate that they're "taking ownership" of their work?

Beware of Creating the "Airport Experience" for Your Legal Team Users ✈️ Airports have lots of processes, queues, information screens, directional arrows, emails, texts, unique numbers, checks, special documents etc. On the surface, it all gives the impression of extreme organization and information sharing. But as a user, particularly if you only navigate an airport once or twice a year, the experience can be overwhelming, tense, confusing, and frustrating. 😵💫 This made me think that while processes, self-service, and information sharing are critical components of a well-run legal department, there's a danger of overdoing it. We must never forget that the average user's ability to comprehend and interact with our department can easily be compromised by overly complex systems and communication channels. I thought of these watch outs to make sure you're not turning your legal department into a bustling airport terminal: Avoid Multiple System Channels of Entry for Queries: Stick to a streamlined process with one or two clear channels for submitting queries. Having multiple systems can confuse users and lead to queries being lost or mishandled. Keep it simple, and ensure that users know exactly where to go when they need help. 🛣️ Simplify Communication: Be realistic about what your business stakeholders are going to read and digest. Focus on clear, concise, and targeted communication that gets straight to the point. Your goal should be to make information easy to digest and actionable, providing it in the place and at the time they need to consume it. ✉️ Cater to Infrequent Flyers: Even frequent users of legal team services won't fully understand or appreciate the process or what's needed as part of their request. For infrequent or first-time users, it can be even more confusing. Be prepared to tell them upfront exactly what you need from them and why, so you can get the request off to the best start. 🚦 #LegalOps #Legal #Law #Business #Efficiency #Communication

Legal's Capacity Crisis Your legal team isn't under-resourced. It's mis-resourced. I hear this constantly: "We need more lawyers." "The workload is impossible." "We're drowning." But when I work with legal teams to understand where they actually spend their time, the picture becomes clear. A significant portion of "legal work" doesn't need a lawyer at all. One GC told me they needed three more lawyers to handle the volume. After mapping their workflows, we found they could handle substantially more work with their existing team - just by stopping the wrong people doing the wrong things. The breakthrough isn't hiring more lawyers. It's creating systems that protect lawyers' time for actual legal judgement. The most efficient legal functions I've seen operate like emergency departments. Triage everything. Escalate only what needs genuine expertise. Handle routine matters through standardised processes. They've trained the business to self-serve on simple matters. They've automated repetitive tasks. They've ruthlessly eliminated work that creates no value. Result? Lawyers who actually get to be lawyers, not expensive administrators. Stop asking for more resource until you've optimised what you already have. How much of your team's time is spent on work that doesn't require legal qualification? #legalops #legalresourcing #inhouselegal #legalleadership

If you're an AI engineer building RAG pipelines, this one’s for you. RAG has evolved from a simple retrieval wrapper into a full-fledged architecture for modular reasoning. But many stacks today are still too brittle, too linear, and too dependent on the LLM to do all the heavy lifting. Here’s what the most advanced systems are doing differently 👇 🔹 Naïve RAG → One-shot retrieval, no ranking or summarization. → Retrieved context is blindly appended to prompts. → Breaks under ambiguity, large corpora, or multi-hop questions. → Works only when the task is simple and the documents are curated. 🔹 Advanced RAG → Adds pre-retrieval modules (query rewriting, routing, expansion) to tighten the search space. → Post-processing includes reranking, summarization, and fusion, reducing token waste and hallucinations. → Often built using DSPy, LangChain Expression Language, or custom prompt compilers. → Far more robust, but still sequential, limited adaptivity. 🔹 Modular RAG → Not a pipeline- a DAG of reasoning operators. → Think: Retrieve, Rerank, Read, Rewrite, Memory, Fusion, Predict, Demonstrate. → Built for interleaved logic, recursion, dynamic routing, and tool invocation. → Powers agentic flows where reasoning is distributed across specialized modules, each tunable and observable. Why this matters now ⁉️ → New LLMs like GPT-4o, Claude 3.5 Sonnet, and Mistral 7B Instruct v2 are fast — so bottlenecks now lie in retrieval logic and context construction. → Cohere, Fireworks, and Together are exposing rerankers and context fusion modules as inference primitives. → LangGraph and DSPy are pushing RAG into graph-based orchestration territory — with memory persistence and policy control. → Open-weight models + modular RAG = scalable, auditable, deeply controllable AI systems. 💡 Here are my 2 cents- for engineers shipping real-world LLM systems: → Upgrade your retriever, not just your model. → Optimize context fusion and memory design before reaching for finetuning. → Treat each retrieval as a decision, not just a static embedding call. → Most teams still rely on prompting to patch weak context. But the frontier of GenAI isn’t prompt hacking, it’s reasoning infrastructure. Modular RAG brings you closer to system-level intelligence, where retrieval, planning, memory, and generation are co-designed. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

Select the best from 500+ RAG pipelines without breaking a sweat 😎 Sounds crazy, right? Most teams pick one RAG setup, hope it works, and ship. Then production hits and everything falls apart. Multi-hop queries fail. Long documents return garbage. Responses flip between great and terrible. Here's what changed my approach: AutoRAG. Think of it like tuning hyperparameters for your ML model. AutoRAG does the same thing for your entire RAG pipeline. It tests chunking methods, retrieval types, and rerankers against your actual data. Then picks the winner. Here's how you get 500+ pipeline options: 4 chunking types (split by tokens, sentences, meaning, or hierarchy) × 3 retrieval methods (keyword search, semantic search, or both combined) × 3 query tricks (rewrite questions, expand them, or break them into parts) × 16 reranking models (Cohere, BGE, ColBERT, FlashRank, and 12 more) = 576 unique combinations You feed AutoRAG two things: → Your documents → Question-answer pairs you know are correct It runs the competition. Tests every combo. Measures accuracy with real numbers. What shocked me: The setup I thought would win came in 4th place. The actual winner? 23% better accuracy on our queries. And it wasn't even close. Sentence chunks + keyword-semantic fusion + BGE reranker crushed my "obvious" choice of semantic chunks + vector search + Cohere for that use case. Not always, what you think would be right, would be right, right? 😉 Detailed explanation of the architecture and how it works is in the carousel 👇 What's your biggest RAG failure that made you rethink everything? Drop the story below, I'm compiling production lessons for next week's breakdown. Follow me, Bhavishya Pandit, for practical AI insights from the trenches 🔥

Your team should be selling. Instead, they’re drowning in admin work. Sales reps just want to sell. But when we sit down with them, it’s always the same story. They’re buried in 20 screens, clicking through fields that don’t help them close deals. And if it takes that much effort? They’ll stop using the CRM altogether. Not because they’re lazy. But because the system wasn’t built for them. Here’s what we see over and over—and how to fix it: 1) Too Much Manual Data Entry No rep wants to spend their time: • Logging calls • Typing out notes • Moving data between tools They’re here to sell—not push buttons. Fix it by automating everything you can. Use tools like Gong & QuickBooks to log calls automatically and sync invoices. The less manual data entry they do, the more time they have to close deals. 2) Clunky CRM Layouts If it takes 20 clicks to close a deal, they’re not going to use the system. Most CRM setups are bloated with fields that reps don’t care about. Half of them aren’t even for sales. The answer: fewer clicks, better layouts. • Two clicks, max • Only show fields relevant to their role 3) Tools Aren’t Integrated Reps live in: • Outreach • Gong • Slack If none of these tools talk to Salesforce, they’re forced to double their work. That kills adoption. Integrate the tools they already use. If it doesn’t make their job easier, it doesn’t belong in the stack. 4) The CRM Feels Like a Surveillance Tool Managers care about dials. Reps care about closing deals and hitting quota. If the CRM only tracks activity, it’s just another micromanagement tool. Fix it by making the CRM valuable for the rep. • AI-driven next steps • Deal insights • Sales shortcuts that actually help them win It has to feel like a competitive advantage—not a chore. 5) Training That Doesn’t Address Real Gaps Most training is based on gut feelings: “Reps need to know this.” But it doesn’t actually address the biggest problems. Fix it by making training metric-driven. If reps aren’t filling out critical fields, start there. Focus on what actually moves deals forward. Make the CRM work for reps, and they’ll actually use it. Make it a burden, and they’ll find ways around it. Enjoyed this post? Follow Jordan Nelson for more insights on making CRMs actually work for sales teams.