A base pattern agent for planning B2B projects. It extracts key planning details from user conversations, checks for relevance, and then leverages planning tools to generate a complete project plan.
This agent base pattern helps Ai builders empowers AI agents to work alongside humans to generate well-informed, actionable plans. It provides capabilities such as detailed information extraction, validation of planning inputs, and can include interactive planning tools that enable agents to collect and process key data from user conversations.
Planning agents help Go-To-Market team members craft detailed, actionable plans with structured data. By leveraging this agent, users receive a structured and efficient process that converts raw, conversational inputs into meaningful planning details.
The agent workflow begins with a user requesting something along the lines of "Let's plan X". This triggers the extraction of key details from the conversion. Examples might include client name, project scope, and start/end dates from the conversation. It then classifies these details to check their relevance against the latest user input and prompts for clarification if changes are detected.
Following this, specialized planning tools are deployed—this step may include UI components within the agent chat interface, enabling human users to make selections on, for example, scheduling options, resource allocations, and budget estimations.
Finally, all planning outputs are consolidated into a comprehensive plan document that may be used for human consumption and/or with downstream agents.
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Use this example as a starting point, not a fixed recipe.
Copy the flow above as a prompt, markdown, skill, or mermaid diagrom. Adjust the steps, tools, agent and human involvement to fit your real workflow. Then test where users actually get value or drop off.
Extract the following information for a B2B plan: client name, project scope, start date (YYYY-MM-DD), end date (YYYY-MM-DD), and number of stakeholders. If the client name or project scope is missing, ask: 'Please specify the client name and project scope for the plan.'
Given the extracted plan details: {planDetails}, and the recent conversation context: {messages}, determine if these details are still relevant for the current planning request. Return a flag 'isRelevant' as true or false.
Using the confirmed plan details: {planDetails}, produce a detailed planning output that includes a timeline, resource recommendations, and an estimated budget.