Task Workflow Modeling

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

One of the most promising directions in software engineering is merging stateful architectures with LLMs to handle complex, multi-step workflows. While LLMs excel at one-step answers, they struggle with multi-hop questions requiring sequential logic and memory. Recent advancements, like O1 Preview’s “chain-of-thought” reasoning, offer a structured approach to multi-step processes, reducing hallucination risks—yet scalability challenges persist. Configuring FSMs (finite state machines) to manage unique workflows remains labor-intensive, limiting scalability. Recent studies address this from various technical approaches: 𝟏. 𝐒𝐭𝐚𝐭𝐞𝐅𝐥𝐨𝐰: This framework organizes multi-step tasks by defining each stage of a process as an FSM state, transitioning based on logical rules or model-driven decisions. For instance, in SQL-based benchmarks, StateFlow drives a linear progression through query parsing, optimization, and validation states. This configuration achieved success rates up to 28% higher on benchmarks like InterCode SQL and task-based datasets. Additionally, StateFlow’s structure delivered substantial cost savings—lowering computation by 5x in SQL tasks and 3x in ALFWorld task workflows—by reducing unnecessary iterations within states. 𝟐. 𝐆𝐮𝐢𝐝𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: This method constrains LLM output using regular expressions and context-free grammars (CFGs), enabling strict adherence to syntax rules with minimal overhead. By creating a token-level index for constrained vocabulary, the framework brings token selection to O(1) complexity, allowing rapid selection of context-appropriate outputs while maintaining structural accuracy. For outputs requiring precision, like Python code or JSON, the framework demonstrated a high retention of syntax accuracy without a drop in response speed. 𝟑. 𝐋𝐋𝐌-𝐒𝐀𝐏 (𝐒𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐰𝐚𝐫𝐞𝐧𝐞𝐬𝐬-𝐁𝐚𝐬𝐞𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠): This framework combines two LLM agents—LLMgen for FSM generation and LLMeval for iterative evaluation—to refine complex, safety-critical planning tasks. Each plan iteration incorporates feedback on situational awareness, allowing LLM-SAP to anticipate possible hazards and adjust plans accordingly. Tested across 24 hazardous scenarios (e.g., child safety scenarios around household hazards), LLM-SAP achieved an RBS score of 1.21, a notable improvement in handling real-world complexities where safety nuances and interaction dynamics are key. These studies mark progress, but gaps remain. Manual FSM configurations limit scalability, and real-time performance can lag in high-variance environments. LLM-SAP’s multi-agent cycles demand significant resources, limiting rapid adjustments. Yet, the research focus on multi-step reasoning and context responsiveness provides a foundation for scalable LLM-driven architectures—if configuration and resource challenges are resolved.

WorFBench: unified Agentic Workflow Generation Benchmark with miscellaneous scenarios and intricate graph-structured workflows. A set of subtasks with execution dependencies is typically referred to as a workflow. Workflows can serve as an intermediate state for solving complex tasks, aiding agents in bridging the gap between tasks and specific executable actions.Existing workflow evaluation frameworks either focus solely on holistic performance or suffer from limitations such as restricted scenario coverage, simplistic workflow structures, and lax evaluation standards. To address it, this paper introduce WORFBENCH, a unified workflow generation benchmark and WORFEVAL, a systemic evaluation protocol. 𝗞𝗲𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀: - propose WORFBENCH, a unified workflow generation benchmark with multi-faceted scenarios and complex workflow structures - introduce WORFEVAL, using effective subsequence and subgraph matching algorithms to evaluate the workflow generation ability of LLM agents from both chain and graph structures 𝗪𝗢𝗥𝗙𝗕𝗘𝗡𝗖𝗛 i) Task formulation - Given a specific task and a candidate action list, our goal is to enable the language agents to generate a graph-structured workflow, where the nodes in the workflow satisfy the minimum executable granularity ii) Benchmark Construction - function call data is collected from ToolBench and ToolAlpaca, - For Embodied tasks, collected EACT format gold trajectories of ALFWorld, WebShop from ETO, OS from AgentInstruct - introduce problem-solving tasks like math, commonsense, and multimodal reasoning tasks from LUMOS and WikiHow iii) Quality Control - order of nodes is logical, is guaranteed when constructing the node chain based on the sequence of gold function calls - For checking if each node accurately decomposes the task, used each synthesized node as a query to retrieve the function list - filtered out 15.36% data through the quality control for the node chain - For workflow graph, discarded 29.77% of data points where topological sorting results not aligned with node chains 𝗪𝗢𝗥𝗙𝗘𝗩𝗔𝗟 - quantitatively evaluate both the node chain and workflow graph using restrict algorithms - calculated similarity matrix between gold workflow nodes and edges, and agent predicted nodes and edges - Since a predicted node may match multiple gold nodes and a gold node may be matched by multiple predicted nodes, utilized a max-weighted bipartite matching algorithm to find best matches 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 - distinct gaps between sequence planning capabilities and graph planning capabilities of LLM agents, with even GPT-4 exhibiting a gap of around 15% - ability to predict graph-structured workflows falls far short of real-world requirements, with even GPT-4 only achieving a performance of 52.47% 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/epAgYS2P 𝗖𝗼𝗱𝗲: https://lnkd.in/e2RDYPMg 𝗗𝗮𝘁𝗮𝘀𝗲𝘁: https://lnkd.in/eJncc2VH

agents that learn your workflows > agents that relearn you every day. I’m sharing a standout research report: Log2Plan, an adaptive GUI automation framework powered by task mining. It learns from real interaction logs, builds a reusable plan, and then adapts each step to the live screen. Think: global plan + local grounding, so agents get more reliable the longer you use them. ↳ Why this matters for UX/UI: ➤ Personalization without hero prompts, the system internalizes how you work (file paths, naming, exception paths). ➤ Recoverable runs, step-level checks and quick human-assist beats brittle macro replays. ➤ Transparent actions, structured plans you can read, audit, and improve. ➤ Resilience to UI drift, intent stays stable even when buttons and layouts move. ↳ What’s actually new here: ➤ Task mining turns messy click/keystroke logs into reusable “Task Groups” (ENV / ACT / Title / Description). ➤ Retrieval-augmented planning pulls the right pieces for a new goal, then the local planner fits them to the current screen. ➤ A clear separation of plan vs. interaction that reduces token bloat and flaky screenshot reasoning. ↳ Try this week (operator’s cut): ➤ Pick one high-volume desktop flow (e.g., monthly report collation). ➤ Curate 2–3 clean traces into “Task Groups.” ➤ Define success metrics (success rate, sub-task completion, time per task, assist rate). ➤ Add human-assist checkpoints for sensitive steps and ship a small pilot. Follow for more UX/UI & AI implementations. Re-share with your network.

𝗧𝗵𝗿𝗲𝗲 𝘁𝗼𝗼𝗹𝘀. 𝗧𝗵𝗿𝗲𝗲 𝗽𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝗶𝗲𝘀. 𝗧𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝘀𝗻'𝘁 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀. 𝗜𝘁'𝘀 𝗵𝗼𝘄 𝗲𝗮𝗰𝗵 𝘁𝗼𝗼𝗹 𝘄𝗮𝗻𝘁𝘀 𝘆𝗼𝘂 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸. Yesterday's episode covered the five problems orchestration solves.  Today: how Airflow, Dagster, and Prefect approach those problems from fundamentally different mental models. 𝗔𝗶𝗿𝗳𝗹𝗼𝘄: 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 → You define DAGs with tasks and timing. The scheduler drives execution. → Mature, battle-tested, largest community. The default in most enterprises. → Strongest for: teams that think in schedules, batch coordination, and time-based SLAs. → Trade-off: dynamic workflows and data-aware triggers require workarounds. Recent dataset-driven triggers are closing this gap. 𝗗𝗮𝗴𝘀𝘁𝗲𝗿: 𝗮𝘀𝘀𝗲𝘁-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 → You model data assets, not tasks. Dependencies flow from what the data IS, not when it runs. → Built-in lineage, type checking, and incremental materialization. → Strongest for: analytics engineering teams that think in data products and ownership. → Trade-off: smaller community, steeper learning curve for teams used to task-based thinking. 𝗣𝗿𝗲𝗳𝗲𝗰𝘁: 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 → You define flows and focus on how tasks execute, retry, and recover. → Lightweight Python decorators. Strong runtime flexibility and dynamic workflows. → Strongest for: ML pipelines, dynamic fan-out patterns, teams that need execution-first design. → Trade-off: less opinionated about data modeling. You bring your own structure. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻: Feature parity is increasing. All three can support batch, event-driven, and dynamic workflows. The choice comes down to how your team naturally reasons about data systems. Choose the model your team can reason about under failure. → Think in schedules? Airflow. → Think in data assets? Dagster. → Think in execution patterns? Prefect. Which mental model matches how your team actually builds? #DataEngineering #Orchestration #DataPipelines

I'm not surprised MIT's report shows 95% of AI prototypes fail. This summer, Forerunner automated 2 critical workflows for our research and diligence efforts. The patience and trial & error required far exceeded our expectations. The results? AWESOME. No doubt AI is powerful. After automating the workflows, I would say the power today is one part potential and one part reality. Building an AI workflow is still quite difficult, especially if you are not technical. So how did we do it? Here's an overview of our first step. 1️⃣ Define workflows 2️⃣ Break workflows down into tasks 3️⃣ Define automation evaluation criteria 4️⃣ Evaluate automation potential & impact 5️⃣ Prioritize tasks for automation The first principle thinking required here set us up for success. In fact, the findings of MIT's report supports this approach -- "pick one pain point, execute well, and partner smartly with companies who use their tools." Across all of our research & diligence workflows, we separated our workflows into 120+ tasks and selected five characteristics to evaluate automation potential & impact. 1️⃣ Task requires a unique Forerunner lens 2️⃣ Automating task would save significant time 3️⃣ Task is routine & frequent 4️⃣ AI can create desired output 5️⃣ Task is essential to building expertise Having this framework clarified what tasks made sense to automate first and, as importantly, what tasks did not make sense to automate today (or possibly ever). We believe certain tasks are more important for skill & perspective building even if they could be automated. More to come on what tool we selected, how we automated the workflows, and what we learned along the way -- this is where the patience and perseverance comes in.

AI operators think in workflows, not words. Stop tweaking prompts. Start designing systems. Months of optimization led me to the breakthrough strategy of focusing on workflow design. Here's how it reshaped the game: Marginal gains. Infinite loops. Shaky outcomes. Then it hit me: the flaw wasn’t in my prompts... it was in the architecture. The Prompt Engineering Dilemma: Solitary prompts are delicate. They shine in trials but crumble quietly in real-world use. Context changes. Edge cases appear. The model confidently produces garbage. You can't solve architectural problems through prompt engineering. The Prompt Architecture Shift Stop asking: "How do I improve this prompt?" Start by asking: "How do I design a system for graceful prompt failures?" This involves: Decomposition Break a complex task into simpler ones. Each prompt handles one function, and together they accomplish what a single prompt can't. Validation Layers The output of every prompt gets checked before the next step: - Does this look right? - Does it match the expected format? - Does it contain the required elements? Failures get caught. Reruns happen automatically. Bad outputs never propagate downstream. Context Management What information does this prompt actually need? - Not everything. - Not the whole document. - Just the relevant context for this specific task. Smaller context windows. More focused instructions. Better outputs. Fallback Paths: What's the plan for model errors? Human help. New prompts. Quick recovery. Our design handles setbacks. Failures are expected. Prompt engineering refines; prompt architecture shapes the vision. The other makes entire workflows reliably excellent. The operators who win at AI aren't better at writing prompts; they're better at designing systems where prompts are just one component. What's one workflow you've built that chains multiple AI calls together?

Partner managers spend hours on work that could run automatically. Compiling reports, drafting repetitive emails, copying data between systems. One-off prompts in ChatGPT won't get you ahead in 2026. What separates effective AI adoption from experimenting is building systematic workflows that run while you're doing other things. The PartnerFlow Automation Canvas is our framework for designing those workflows. Five components you need to map before building anything: problem definition, inputs, outputs, process flow, and security. The guide includes the canvas template and examples for common partnership tasks. No hype about transformation. Just practical steps for moving from occasional AI use to workflows that actually scale your work.

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 👇

The whole point of agentic systems is not just about solving but automating complex workflows. Agentic workflows are quickly becoming the dominant paradigm for AI applications. Agentic workflows commonly coordinate multiple models and tools with complex control logic. What happens when you have to coordinate more complex processes that go beyond a single agent’s scope? This is where agentic workflows come into the picture. An agentic workflow is a multi-step, dynamic process that orchestrates multiple API calls, AI tasks, agents, and even human-in-the-loop steps within a dynamic control graph. The workflow can branch, loop, or change course based on AI-driven evaluations, allowing it to adapt in real time. Rather than embedding all logic inside a single agent, the workflow externalizes decision points and coordinates agents and services. Agentic workflows enable output validation, decision overriding, human oversight, and other observability features out-of-the-box. This is crucial for enterprise uses where governance over autonomous agents is needed. Example use cases: ➟ Threat detection pipelines ➟ Fraud or claims processing ➟ Research assistants coordinating search, summarization, and synthesis. Key elements: ➟ Task Nodes: AI agents, LLM tasks, API calls, database queries, manual review steps ➟ Decision Nodes: AI-driven logic for routing control flow. ➟ Working Memory: Shared state across workflow steps. ➟ Flexible Control Flow: Branching, looping, and fallback paths for dynamic control. Essentially, the workflow provides a structure within which the AI agent can choose different paths or repeat steps as needed. Know more about agentic workflows: https://lnkd.in/gKrJ3ddK Here is my practical guide on building agentic applications/systems: https://lnkd.in/gh5S8KiH Here is my hands-on guide on building agentic workflows: https://lnkd.in/ggCaDm7z