# How OpenAI Puts ChatGPT to Work | DevDay 2026

## Executive summary

OpenAI demonstrated how its teams are leveraging ChatGPT as an 'agentic teammate' to automate complex, time-consuming tasks across product launches, competitive research, and knowledge management. The core concept is 'ChatGPT work,' where users hand off responsibilities to the AI, allowing human experts to focus on high-level strategy, decision-making, and creative work. Key tools introduced include ChatGPT Spaces for collaborative research and Dots for continuous, autonomous monitoring.

## Key takeaways

- ChatGPT as an Agentic Teammate: ChatGPT work is positioned as an agentic teammate that takes on ongoing tasks, allowing users to hand off day-to-day work to free up time for novel ideas or strategic projects. (0:02) (0:04)
- Automating Monitoring and Reporting: For developer social monitoring (Dani), ChatGPT can monitor social feeds and aggregate engagement metrics into a live report, eliminating hours of manual scrolling and data collection. (1:20)
- Scaling Competitive Research: Competitive research (Adrian) can be scaled by putting the full research methodology into a plugin. This allows the AI to investigate patterns, challenge findings, and analyze large datasets (e.g., 90 days of sales call transcripts) without constant human intervention. (2:10)
- Centralized Knowledge Management: Knowledge bases (Casey) can be maintained and fed into coaching sites by having agents contribute research and meeting notes to a shared page, ensuring consistent style and up-to-date information. (3:30)

## Technical details

- ChatGPT Work/Agentic Teammate: The concept of giving ChatGPT a defined task and context, moving beyond simple thought partnership or coding agent use. (0:04)
- ChatGPT Spaces: A collaborative environment where multiple users and agents can work on the same page simultaneously. Research developed within a Space feeds into external sites (e.g., Compete Corner). (4:40)
- Page Instructions: Used to guide agents on a page, ensuring they adhere to consistent working practices and styles, similar to an `.md` file for a codebase. (5:40)
- Dots (Autonomous Agents): A cloud-based, always-on agent that can monitor feeds (e.g., social media), collect data, and perform ongoing responsibilities. Dots can be reached via text, call, or Slack ping, and can access local files. (6:40)

## Practical implications

- Implement automated monitoring workflows for key metrics (e.g., social sentiment, bug tracking) by assigning continuous monitoring tasks to an agent.
- Structure knowledge bases using 'Page Instructions' to enforce consistent documentation standards across engineering teams.
- Use collaborative spaces to centralize research and development findings, allowing multiple engineers to contribute and challenge conclusions in real-time.
- Identify repetitive, data-gathering tasks (like manual report generation or status updates) and hand them off to an autonomous agent (Dot) to free up human capacity for design and architecture.

## Topics

AI Automation, Workflow Optimization, Agentic AI, Knowledge Management, Developer Experience, ChatGPT, Codex, GPT-6 Astra, ChatGPT Space, Dots

Source: https://www.youtube.com/watch?v=SXCfs7mitPo