Multi-Step AI Agent — Plan-and-Execute Workflow Automation

I will build a multi-step AI agent using a plan-and-execute architecture — where the agent first decomposes your complex task into a sequence of sub-steps, then executes each step using the appropriate tool, adapting its plan if a step fails or returns unexpected results, and reporting progress throughout execution. Multi-step tasks that depend on the output of earlier steps to determine later actions require an agent capable of dynamic planning rather than a fixed linear script; the plan-and-execute pattern handles this correctly by allowing the agent to revise its approach when reality diverges from its initial plan.

The agent covers task decomposition logic (breaking your complex goal into ordered sub-tasks), tool integration for each required action across the workflow, execution monitoring with retry and replanning logic for failed steps, progress reporting at each stage, and a final completion report summarising the full execution path and outcome.

Designed for businesses with multi-step processes — research-then-draft-then-review workflows, multi-system data reconciliation, or any task requiring sequential decision-making — who want genuine autonomous execution rather than a rigid linear automation.