Observed Signal · May 24, 2026 · Technical Article · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Plan-and-Solve Agent Architecture: Plan First, Then Execute
This technical article explains the Plan-and-Solve agent paradigm: use an LLM to generate a complete, ordered plan of 3–7 concrete steps (Plan phase), then execute each step sequentially with tool-enabled sub-agents (Solve phase). The author implements the architecture using LangGraph's StateGraph abstraction, showing a Plan node, an Execute node that embeds a ReAct sub-agent (for tool calls like web_search and calculator), a Replan node, and a Finalize node. Demos highlight practical failure modes—information loss when step results are transmitted as short natural-language summaries, planner over-splitting, and tool-specific errors—and propose engineering fixes (structured step outputs, dedicated collected_data state, planner-annotated data flow). The article concludes with five production findings and guidance on when to choose ReAct versus Plan-and-Solve.
Provides practical engineering guidance for building reliable LLM agent workflows (Plan-and-Solve + ReAct) and concrete mitigation patterns for information loss and tool failures; relevant to teams building agentic systems but not a major platform policy or industry-shifting announcement.
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Key Takeaways & Evidence Grounding
- Plan-and-Solve uses two phases: Plan (LLM produces an ordered list of steps without tool calls) and Solve (execute steps one-by-one with tools).
- The author implements Plan-and-Solve with LangGraph as a StateGraph where nodes read and write a shared PlanSolveState.
- The Plan node's planner prompt enforces 3–7 concrete actionable steps and requires the final step to synthesize results.
- The Execute node embeds a ReAct sub-agent that can call tools (examples: web_search, calculator) and returns step_result and completed_steps.
- Five engineering findings: planners over-plan, natural-language step summaries cause information loss, tool failures can be recoverable, replanning has trade-offs, and Plan-and-Solve composes with ReAct.
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AI Agents: When LLMs Take Actions
A technical tutorial describing goal-driven AI agents built on large language models. The article distinguishes reactive pipelines from agents that plan, call tools, observe results, and iterate (the ReAct pattern). It includes a Python example Agent class using the anthropic API (model reference: claude-3-5-haiku-20241022), a reusable tool library (calculator, web_search, time, file read/write, python_repl), guidance for planning agents, common agent failure modes and mitigations, an evaluation harness, and reference links to research papers and frameworks (ReAct, Toolformer, AutoGPT, LangChain, LlamaIndex, OpenAI Assistants API). The post is a how-to primer for engineers implementing multi-step, tool-using LLM agents.
Animated Guide to ReAct Agents for AI Reasoning
This article provides an animated explainer for ReAct agents, a pattern that combines reasoning and acting in AI models to solve problems through a Thought-Action-Observation loop. It highlights how plain LLMs struggle with factual queries, but by giving them tools and a loop, they can verify information and produce grounded answers. The guide walks through a live example of an agent answering a population question using search and calculator tools. It also covers common pitfalls like endless loops and cost issues, and variations like plan-and-execute and multi-agent systems. The content is educational, serving as a tutorial for building such agents, making it relevant for AI and automation in marketing technology.
From Prompt Engineering to Agentic AI Systems
This engineering-focused blog post explains Agentic AI—autonomous systems that understand objectives, plan, select tools, execute tasks, observe results, and iterate until goals are met. It defines the four essential building blocks for production agents (Brain/LLM, Tools, Memory, Goal), describes the ReAct Think→Act→Observe loop, and emphasizes planning, memory, observability, and error handling for reliability. The author gives a short code example using LangChain and ChatOpenAI, discusses multi-agent architectures and specialized agent roles, compares orchestration frameworks, and lists an engineering stack of frameworks, vector stores, and infrastructure components used to build autonomous AI systems.
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