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Sculpting a Shapeless Medium
The new coding tool Intent attempts to replace unstructured agent chats with organized, spec-driven workspaces.
What: Intent, developed to improve AI-assisted coding, moves away from standard chatbot interfaces by using specialist agents and project-specific workspaces to maintain structure during complex development tasks.
Why it matters: It addresses the 'shapeless' nature of current AI coding assistants by introducing intentional orchestration, though it introduces new friction points regarding token usage costs for parallelized tasks.
Deep dive
- Intent: A platform for managing AI coding agents using structured workspaces instead of linear chat logs.
- Orchestration: The process of coordinating multiple AI agents to perform complex, multi-step engineering tasks.
- Parallel Tasks: Running multiple agentic workflows simultaneously, which increases speed but significantly spikes token consumption.
- Spec-driven: Development where AI agents adhere to predefined technical specifications rather than open-ended natural language prompts.
Decoder
- Agentic workflow: An AI application structure where the model autonomously breaks down a complex goal into sub-tasks and calls tools to complete them.
- Token costs: The fees charged by AI providers based on the number of character fragments processed, which escalate quickly when running multiple agents at once.
Original article
Intent, a new tool for coding with AI agents, organizes work into workspaces rather than single agent chats, aiming to keep structure in an otherwise shapeless medium. Rather than one general chatbot, the tool uses specs, specialist agents, and bundled workspaces so people can steer AI work instead of just prompting it. Since launch, users have shown mixed reactions — some confused by the new orchestration model, others finding it supercharges their workflow, while rising token costs from parallel tasks have emerged as an unexpected tradeoff.
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