Automating workflows with o3

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Automating Multi-Step Workflows with o3

Introduction

In the latest version of o3, OpenAI has taken reasoning and tool usage to a new level. The most exciting capability introduced is its ability to combine multi-step reasoning with agentic tool use—enabling it to complete complex tasks more autonomously than ever before.


Running a Month-End Variance Report

Let’s walk through a demo of o3 running a month-end variance report.

Step 1: Uploading the Prompt and Data

We begin by asking o3 to generate a variance report based on some dummy department data. Each uploaded spreadsheet contains:

  • Budgeted vs. actual spend
  • Department names
  • Cost centers
  • Forecast figures

Step 2: Task Decomposition

Normally, this workflow would involve:

  1. Harmonizing and analyzing the data
  2. Flagging variances over a threshold (e.g., 7%)
  3. Visualizing the data
  4. Searching the web for benchmarks
  5. Creating an executive summary for the CFO

o3 is now capable of automatically completing each of these discrete steps—calling the appropriate tool or performing the right operation at each phase.


Execution and Tool Use

Once the task is sent off, o3 begins its chain-of-thought reasoning, visible in the live output:

  • Data Analysis: o3 parses the uploaded CSV files, writes Python code, and flags ~20 out of 25 lines that exceed 7% variance.
  • Web Search: It searches the internet for benchmarking data from reliable sources (e.g., KPMG).
  • Visualization: It generates interactive graphs visualizing the flagged variances.
  • Insight Generation: o3 provides analysis summaries and citable insights.

Output Generation

In about a minute, o3 produces:

  • Flagged variances (highlighted)
  • Visual breakdowns and key takeaways
  • Benchmarks from verified sources
  • Executive summary
  • Slack-ready post for the CFO

Conclusion

This example highlights the shift from reactive AI to agentic AI. o3 didn’t just wait for instructions, it proactively:

  • Broke the task into sub-goals
  • Chose the best tools for each sub-goal
  • Coordinated them autonomously
  • Delivered a high-quality report in a fraction of the usual time

This is not just generative AI; this is agentic execution at scale.