Agent Orchestration

How AI agents collaborate to build software

The one prompt (That built this whole page)

Build me a visual, interactive HTML page that demonstrates how our AI agent orchestration pipeline works. On the left side, create an animated visualization with glowing nodes, particles, and connections that gradually builds up while explaining each step of the pipeline through guiding text. Use jungheonlee.com as visual inspiration for the animation style. On the right side, simulate the actual agent workflow (see attached screenshot): a VS Code-style chat interface where messages appear with a typewriter effect, showing subagents being invoked, tools being used, files being edited, and results coming back. Each agent node should display a live token context window counter that increases as the agent works, and accumulates when an agent is called more than once. Show a cumulative total at the top to illustrate what a single agent would need if it handled everything alone. The page should auto-play like a video when pressing play. Show the complete flow from the initial prompt through strategy validation, architecture design, security review, implementation, QA testing, and documentation. Include the feedback loops where the security reviewer and QA tester reject work and route it back for corrections. The target audience is non-technical, so pacing needs to allow time to read and understand each step. Keep all text easy to read against the dark background with enough contrast to be comfortable on the eyes. The full walkthrough should run approximately 10 minutes. Once complete and tested, connect to ssh groupforgevm and publish the website using the existing caddy. A-record orchestrator.groupforge.be has been created already.
✕ Pasted Image

~10 minute walkthrough

Cumulative context (single-agent equivalent)

0 tokens

Why does this number matter?

Context rot is the degradation of an LLM's ability to follow instructions and use information accurately as the conversation grows longer and the context window fills up. As more tokens accumulate (tool outputs, file contents, prior reasoning) the model's attention dilutes, causing it to forget earlier instructions, hallucinate, or contradict itself. This is compounded by the primacy-recency bias: LLMs reliably attend to tokens near the beginning and end of the context but lose grip on information buried in the middle (sometimes called the "lost in the middle" problem). A 20-page context with the critical detail on page 12 performs measurably worse than one where that detail sits in the first or last page.

Agent orchestration directly combats this by splitting work across multiple short-lived, focused sub-agents instead of one monolithic conversation. Each sub-agent gets a clean, minimal context containing only the system prompt and the specific slice of information it needs, no accumulated history bloat. The orchestrator summarizes each agent's output into a compact result before passing it to the next stage, effectively "resetting" context rot at every handoff. This keeps every agent operating in the sweet spot of its context window, where attention is strongest. The net effect is that each reasoning step is sharper, instructions are followed more faithfully, and the overall pipeline produces higher-quality results than a single agent grinding through the same work in one sprawling conversation.

◆ Copilot Agent

● Idle