The Token Bloat Problem in AI Planning
teodorio · x · 2026-07-10
The author notes that when AI tools are widely used for non-code tasks like documentation, project planning, legal, and sales, a problem rarely seen in coding emerges: there is no natural token limit. Excessively long code directly harms execution and readability, but in documents and proposals, models often keep expanding, repeating, and diluting information, resulting in more content without necessarily adding value.
They believe this issue will be magnified in agent collaboration: agents scattered across multiple documents, databases, and repositories will repeatedly summarize and lose information, requiring even more tokens to reach a solution. The author isn't against using AI for execution, but believes that heavily utilizing it for planning and architecture is highly inefficient, and that more efficient compaction at scale will be needed in the future.
Related event: AI in Non-Coding Tasks Causes Information Thread Bloat(2 posts)→
More from coding & agent
- HeyGen adds a media-sourcing skill for coding agents with 75k images and 10k tracks — HeyGen · 2026-07-22
- Agent search bottlenecks are now about variance, not raw latency — rohanpaul_ai · 2026-07-22
- LangSmith adds tracing for Pipecat, LiveKit, OpenAI Realtime, and Gemini Live — LangChain · 2026-07-22
- An MCP server signs every AI agent tool call into a verifiable Merkle chain — Funky_Chicken_22 · 2026-07-22
- Annotated transcript of a Claude Code team interview is now available — trq212 · 2026-07-22
- Claude Code skill uses 10 Markdown rules to make outputs ADHD-friendly — alex_verem · 2026-07-22