Sequential Task Planning

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.

A lot of what people call “AI agents” are just tool loops with no real planning. The pattern looks like this: • The LLM reasons (a bit). • Calls a tool. • Reads the result. • Calls another tool. • Repeats.    If there’s no explicit planning step and no goal decomposition, that’s not really an agent. It’s just reactive behavior wrapped in a loop. This works for simple tasks. But as soon as workflows get more complex or multi-tool, it falls apart. The missing piece? Structured planning. That’s where patterns like ReAct and Plan-and-Execute come in. While building out Nova, a deep research agent you’ll learn how to build in our upcoming AI agents course, we started with ReAct. This makes decisions one step at a time one step at time. And due to its sequential nature, it’s often slow. It also requires robust tooling and loop control to prevent infinite loops or getting stuck. The real magic happens with Plan-and-Execute… This approach creates a full plan up front, then executes it efficiently. Hence, it’s ideal for tasks that: • Follow a predictable sequence • Can parallelize actions • Need lower latency and cost    Here’s the core structure: 𝟭/ 𝗣𝗹𝗮𝗻𝗻𝗲𝗿 The strategic brain. It takes a goal and decomposes it into clear, ordered steps. Example: “Generate queries → run searches → scrape results → summarize findings.” 𝟮/ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿 The quality gate. Checks if the plan is coherent, feasible, and aligned with the goal before anything runs. 𝟯/ 𝗘𝘅𝗲𝗰𝘂𝘁𝗼𝗿 The workhorse. Runs the validated plan (sequentially or in parallel), gathers results, and feeds them back. Then the cycle repeats: Plan → Evaluate → Execute → Decide → Replan if needed. In production, this structure: • Improves efficiency • Reduces latency • Makes debugging and monitoring simpler • Enables smarter orchestration    But it’s not a silver bullet. For highly exploratory tasks, you still want ReAct-style step-by-step planning. For structured workflows, Plan-and-Execute shines. The real skill is knowing when to use which pattern and how to combine them. If you want a deeper breakdown of ReAct vs Plan-and-Execute (with code and real-world examples), I just published a new lesson in the AI Agents Foundation series on Decoding AI Magazine: Check it out here → https://lnkd.in/d9BVvj7P

Are we overcomplicating our AI solutions with agents when a simple workflow could achieve the same goals more cost-effectively and efficiently? Among all the writeups I’ve read on agentic systems, the one "Building effective agents" by Anthropic stands out as my favorite as it delivers a powerful message: 𝑇ℎ𝑒 𝑠𝑖𝑚𝑝𝑙𝑒𝑠𝑡 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 𝑜𝑓𝑡𝑒𝑛 𝑜𝑢𝑡𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑠 𝑡ℎ𝑒 𝑚𝑜𝑠𝑡 𝑐𝑜𝑚𝑝𝑙𝑒𝑥 𝑑𝑒𝑠𝑖𝑔𝑛. 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫? 🔸 Customers seek easy-to-use and reliable solutions to their problems. They care less about the underlying technology. 🔸 Businesses need to save costs and drive revenue. They care less about unnecessary complexity and more about solutions that deliver measurable results and maximize ROI. In a nutshell, at the core of every successful AI solution lies a fundamental truth: 𝐸𝑛𝑑 𝑔𝑜𝑎𝑙 𝑖𝑠 𝑡𝑜 𝑐𝑟𝑒𝑎𝑡𝑒 𝑣𝑎𝑙𝑢𝑒, 𝑛𝑜𝑡 𝑐𝑜𝑚𝑝𝑙𝑒𝑥𝑖𝑡𝑦. And agents come with a tradeoff: 🚀 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 but at the cost of ⏱️ 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 and 💰 𝐜𝐨𝐬𝐭𝐬 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐫𝐞𝐚𝐥𝐢𝐭𝐲 𝐜𝐡𝐞𝐜𝐤: 🔸If your tasks follow predictable, predefined steps → A 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 is likely all you need. 🔸If your task is open-ended, with dynamic steps and tools → An 𝐚𝐠𝐞𝐧𝐭 might make sense. 𝐖𝐡𝐚𝐭 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝: Practical patterns that solve real problems before needing agents: 🔗 𝐏𝐫𝐨𝐦𝐩𝐭 𝐂𝐡𝐚𝐢𝐧𝐢𝐧𝐠 What is it: breaking tasks into sequential steps Use when: tasks are predictable and can be broken into smaller subtasks Example: document drafting steps 🚦𝐑𝐨𝐮𝐭𝐢𝐧𝐠 What is it: classifying input for specialized handling Use when: inputs have distinct categories for tailored processing resources for specific needs Example: sorting customer queries ⚡𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 What is it: splitting tasks for simultaneous processing Use when: subtasks are pre-defined and set for concurrent processing Example: legal contract section analysis 🤹♂️ 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫-𝐖𝐨𝐫𝐤𝐞𝐫𝐬 What is it: central LLM delegates subtasks dynamically User when: subtasks aren't pre-defined, but determined by the orchestrator Example: multi-file code updates 🔄 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐨𝐫-𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫 What is it: one LLM creates; another evaluates Use when: iterative improvement provides measurable value Example: refining translation accuracy 🧠 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐠𝐞𝐧𝐭 What is it: LLM autonomously plans and executes tasks Use when: tasks are complex, open-ended, and require dynamic decisions Example: budget friendly trip booking 𝐌𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲 Often, less is more, and simple is better. Anthropic link: https://lnkd.in/deKWxeQi #aiagents #llm

𝗘𝘃𝗲𝗿 𝘁𝗿𝗶𝗲𝗱 𝗺𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝗱𝗼 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗮𝘀𝗸𝘀 ���𝗻 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲? 𝗜𝘁'𝘀 𝗵𝗮𝗿𝗱𝗲𝗿 𝘁𝗵𝗮𝗻 𝗶𝘁 𝗹𝗼𝗼𝗸𝘀. AI agents are great at making decisions, but most business processes often need predictable, sequential steps. Think of analyzing a document: first summarize it, then extract key points, then generate recommendations. This predictability demands a stable, repeatable sequence of prompts. My latest n8n template demonstrates three powerful approaches to prompt chaining: 1️⃣ 𝐍𝐚𝐢𝐯𝐞 𝐂𝐡𝐚𝐢𝐧𝐢𝐧𝐠: Simple but slow. Connect LLMs in sequence. 2️⃣ 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Maintains memory between steps. Scalable but still sequential. 3️⃣ 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Maximum speed. Runs all prompts simultaneously. No shared memory between steps. 𝐃𝐨𝐧'𝐭 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐭𝐡𝐚𝐭 𝐛𝐫𝐞𝐚𝐤 𝐮𝐧𝐝𝐞𝐫 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞. 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐭𝐡𝐞𝐦 𝐫𝐢𝐠𝐡𝐭 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐬𝐭𝐚𝐫𝐭!