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Multi-agent handoffs: how to stop AI agents losing context

Downloads | AI agents, AI playbooks, AI-ready data and knowledge, Technology and data leaders

A marketing agency’s research agent worked well on its own. Chained to a copywriter agent, it fell apart: the copywriter treated a new VP’s Amazon background and a 15% share price fall as equally important, and wrote a cost-cutting email that missed the point.

Three things vanish at a handoff: the reasoning behind each point, how confident the claim is, and the rules that stop obvious mistakes. The Context Envelope carries them across.

The Context Envelope

  • Facts what was found
  • Reasoning why it matters
  • Constraints how to frame it and what to avoid
  • Confidence how strong each signal is

The four layers every handoff should carry

Why do AI agents lose context?

Agent chains are treated like relay races: pass the text and the job is done. But the next agent reads a flat list of points with no sense of which matter. The reasoning, the confidence level and the constraints stay with the first agent.

What is a Context Envelope?

A structured handoff that separates facts from intent. It holds the facts, the reasoning about which matter, the constraints on framing, and a confidence rating for each signal. In the agency’s example, the envelope said the Amazon hire was the hook and the share price was noise.

How do you build it into your agents?

Give the first agent an encoder prompt that requires the four fields, and the second a decoder prompt that follows the constraints strictly and favours high-confidence signals. In production the agents pass the envelope directly as structured data.

Key takeaways

  • Do not hand off raw text. Pass facts, reasoning, constraints and confidence.
  • Rate every claim. High confidence needs two sources; inference is low.
  • Use an avoid list. Even with little context it stops the obvious mistakes.
  • Set the envelope before you chain. The chain fails where agents join.

What’s in the full guide

If you are chaining agents together, the full guide gives you the format and the prompts:

  • The four-layer Context Envelope spec
  • The encoder prompt for the first agent
  • The decoder prompt for the second
  • A worked example, before and after
  • The JSON structure agents pass in production

This guide first appeared in aiblBRIEF. Read the original article: Why your agents keep losing context (and how to fix it).

Make your agent handoffs hold together

Multi-agent handoffs: how to stop AI agents losing context
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