Amp AI agents are being repositioned to do more than generate code: the startup is integrating a suite of OpenAI models to push agents into planning, design, writing and product growth tasks across the full lifecycle of a project. The company’s CEO, Quinn Slack, says model selection is driven by what delivers the best results for users, and that OpenAI models already account for the majority of tokens consumed on Amp’s platform.
Slack and his team see recent model improvements as an opening to move agents beyond narrow developer tasks. Where agents once focused primarily on producing snippets of code, Amp is now using models to help teams decide what to build, create production-ready code, and generate marketing, design and documentation assets. This shift reflects an intent to have agents assist at multiple stages of product work rather than only during implementation.
Model choice is a practical consideration for Amp. Slack told OpenAI that transparency around model safeguards made it straightforward for Amp to adopt OpenAI models and for customers to understand when safeguards are applied. That confidence in model governance complements Amp’s approach of selecting the specific model that best serves a given task.
To address a practical barrier to broader agent usage, Amp integrated Sign in with ChatGPT so users can access more tokens and OpenAI models without constantly tracking consumption. Slack described token costs as a limiting factor for agent-driven workflows: some Amp customers consume as many as 10 billion tokens per week. When users worry about incremental token charges, they may hesitate to let agents run longer or tackle larger tasks. By reducing the need for detailed token accounting, Sign in with ChatGPT is intended to give users a longer “leash” for agents, encouraging experiments and longer-running workflows that reveal more of what agents can do.
For higher-fidelity writing and design output, Amp relies on GPT-6 Astra. The company’s roughly 20-person team already uses agents to support marketing, onboarding and documentation. Slack said the team is selective about tone and visual style, and that GPT-6 Astra has met those internal standards when producing documentation and design pages. He also shared personal examples: after providing house plans to GPT-6 Astra, the model helped generate a 3D Blender model that his family used to create games—an outcome Slack suggested would not have been possible a few months earlier.
Amp has also built capabilities for real-time interaction. The platform’s conversational assistant, Puck, helps users launch and manage other agents. Puck can be asked to investigate a bug, coordinate several agents, or retrieve prior conversations about technical issues. When Calling Puck is powered by GPT-Live-1, users can interact with the assistant in real time—even from wearable devices—letting them monitor and manage agent workflows while away from a computer. Slack suggested scenarios like checking on agents during a run, enabling continuity in workflows outside the office.
Speed has been another area of focus. Amp has tested an Ultrafast mode alongside GPT-6 Astra and found that faster responses alter how people interact with agents. Quicker turnaround reduces waiting time between tasks, can let users manage fewer concurrent agents, and gives agents more opportunities to self-check before returning results. Amp sees increased speed as an enabler for smoother, more continuous agent-driven work.
Taken together, these changes reflect Amp’s broader product roadmap: expand what agents can do before and after coding so creators can move ideas further without manually completing every intermediate step. The company frames this as an open question about what builders will create as agents take on more responsibilities; by reducing token friction and pairing different OpenAI models to suit different jobs, Amp aims to surface new use cases for agent-assisted product work.
For builders and product teams, the immediate takeaway is practical: Amp AI agents are being positioned to support planning, design and post-launch activity as well as code generation. The combination of model selection, real-time interaction, faster response modes and Sign in with ChatGPT is meant to reduce operational friction and invite users to experiment with longer, more autonomous agent workflows. How creators will leverage that expanded capability remains to be seen, but Amp’s changes are designed to make it easier for teams to find out.
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