7-Step Roadmap: Building Multimodal AI Agents from LLMs to Grounded Systems
MaryamMiradi · x · 2026-08-27
The author outlines a 7-step roadmap for building grounded AI agents from multimodal LLMs, designed to see, read, parse, ground, reason, verify, and escalate. This enables production agents to understand complex inputs like screenshots, dashboards, and medical images.
Key Architecture Steps:
- Step 1: Modality Router: Avoids sending all inputs blindly into one model. It detects the type (text, image, chart, audio, video, table, UI), routes each to the correct path, and separates weak signals from reliable evidence.
- Step 2: Perception Encoder: Prioritizes perception before reasoning. Text becomes tokens, images become patches, audio becomes signals, and video becomes frames, converting non-structural data like charts into usable context.
More from coding & agent
- Indie dev made six figures with 7 AI agent skill files, now free — eptwts · 2026-08-27
- Mixedbread Agent Retrieval Infra Hits 0.05ms p99 Latency — lateinteraction · 2026-08-27
- Open source tool errd selects local context for precise AI debugging — DevilRdx100 · 2026-08-27
- Grok Bot rolls out to all users, managing e-commerce and events — chaitu · 2026-08-27
- Copilot CLI v1.0.81-12 released with WAM support — copilot-cli-release-app[bot] · 2026-08-27
- Cloud Agents vs. Local Machines: Trust and Verification Workflows — hugobowne · 2026-08-27