Preventing context contamination across agents

Why this matters

When agents share state, one agent's mistakes, hallucinations, or half-formed reasoning can contaminate another's context — the second agent treats the first's speculation as fact. This compounds: errors propagate and amplify across the pipeline. Preventing contamination means marking the provenance and confidence of shared context (this is a verified fact vs. this is agent-2's hypothesis) so downstream agents weight it appropriately, and isolating speculative reasoning so it doesn't leak as ground truth. It's the multi-agent version of trust boundaries.

Demo

The demo tags every shared item with provenance and status (verified/hypothesis/raw) so a downstream agent can be instructed to act only on verified items and treat hypotheses as tentative — stopping error propagation.

Try it yourself

1. Run it — the hypothesis is clearly marked so a downstream agent won't treat speculation as fact.
2. Remove the status tags and instruct the decider normally; observe how it may adopt the hypothesis as ground truth.
3. Add a 'confidence' score and have the decider weight items by it.
4. Trace a deliberately-wrong fact from one agent and confirm provenance lets you find which agent introduced it.

Prompt your AI

Use these three in order. Each builds on the one before.

1. Basics & terminology

What is context contamination in multi-agent systems, and how does one agent's error spread to others?

2. Why it works (the mechanism)

Explain how provenance and status tagging (verified vs. hypothesis vs. raw) on shared context prevents downstream agents from treating speculation as fact.

3. Advanced — application & what's next

Design an anti-contamination scheme for a multi-agent research pipeline: provenance tracking, confidence propagation, isolating speculative reasoning, and how I'd detect which agent introduced an error.

References

• Anthropic — Building effective agents
• Anthropic — Reduce hallucinations

When the model call fails. Read the error and decide: fix the request, retry, or fall back. 400/422 (bad params, context-length exceeded), 401/403 (auth / no access to that model), 404 (wrong model id) are fatal — fix and don't retry. 429, 500/502/503, Anthropic 529 (overloaded), and timeouts are transient — retry with backoff. Watch for non-HTTP failures too: finish_reason: "length" truncation (raise max_tokens or continue), safety , malformed JSON / failed tool-call parsing (validate against a schema and repair-retry), and mid-stream disconnects. Always log the provider with the error so you can trace it later.