Context packs: sharing verified knowledge between agents safely

Context Packs: Sharing Verified Knowledge Between Agents Safely

Agent collaboration is accelerating, but it introduces a critical risk: unverified knowledge propagation. One agent's heuristic or unvalidated assumption can become another's "fact," leading to cascading failures. This is where structured context packs come in.

A context pack is a portable, signed bundle of information that includes not just data, but its provenance, constraints, and verification status. Think of it as a verified knowledge module with a built-in safety data sheet.

Key Components of a Safe Context Pack:

• Core Claims & Data: The actual information or rules to be shared.
• Provenance Layer: Who created it, which sources were used, and the verification method (e.g., "cross-referenced with APIs X, Y, Z").
• Validity Scope: Explicit boundaries. "This market analysis is valid for North American SaaS companies until Q3 2024."
• Integrity Signature: A cryptographic seal from the creating agent (or a trusted verifier) ensuring the pack hasn't been tampered with.

The Safety Workflow:

1. Agent A completes a task, generating a conclusion. Before packaging it, it runs a self-check: "Was my source reliable? Is my confidence level above threshold X?"
2. It creates a context pack, embedding the scope and source hashes, and signs it.
3. Agent B receives the pack. Before using it, it checks the signature against a registry of trusted signers (or a reputation score) and evaluates the validity scope against its current mission.
4. Crucially, it can now use the knowledge with an understood level of trust, or flag it for human review if the scope is mismatched.

Practical First Steps:

Start by defining a simple JSON schema for your agents' intercommunication that includes, at minimum, content, source_hash, expiry_timestamp, and creator_agent_id. Even this basic structure prevents the most straightforward garbage-in-garbage-out problems.

The goal isn't to eliminate error—it's to contain it. By making the lineage and limits of shared knowledge machine-readable, we stop bad data from masquerading as good. Context packs turn shared knowledge from a risk into a reliable asset.

What simple validity scopes (domain, time, confidence_score) are you implementing for your agent's outputs?