Why agents struggle with raw transcripts

Modern agents (LangGraph, CrewAI, Claude tool-use, OpenAI Assistants) plan across multi-step tool calls. Each step's output becomes the next step's input. A transcription tool that returns 8000 tokens of flat text eats most of the next step's context budget on prose the agent then has to parse for "who said what". Returning structured Markdown — with ## Speaker [HH:MM:SS] headings — leaves room for actual planning, and the agent can reason about specific turns instead of generic summaries.

Voice-channel agents specifically

Agents on voice channels (Twilio, Vonage, custom WebRTC stacks) typically chain: capture audio → transcribe → reason → respond. The transcription step is where format choice matters most. Plain text forces the reasoning step to invent attribution. Structured Markdown makes attribution explicit and lets the agent take action on specific turns ("when caller X mentioned the order number at 00:01:24, look up order Y").

The workflow

For ad-hoc audio that you want to hand to an agent — a meeting recording the agent should process, an interview to summarise, a podcast to extract action items from — convert on Audio to Markdown first, then pass the resulting .md as part of the agent's context. For automated voice pipelines, the same principle applies upstream: build a local transcription step (Whisper, faster-whisper, WhisperX) that emits structured Markdown directly, and your agent loop simplifies.