examples
High-intent calls to try first.
These are the cases where RPCS-1 is most obvious: a support copilot under pressure, a coding agent in a changing repo, or a research agent handling conflicting evidence. The first useful answer should lead with TI, SG, FT, UE, AR, then the failure-risk score and next test.
Support copilot under pressure
Use this when refunds, billing disputes, policy exceptions, and queue pressure make the agent behave inconsistently.
example request
Use recommend_agent_configuration to diagnose my support copilot.
Task: refund and billing dispute triage
Environment: dynamic, somewhat_predictable, high stakes
Context relevance: medium
Commitment style: cautious
Target platform: anthropicCoding agent in a changing repository
Use this when a coding agent retries too aggressively, changes direction, or commits before it has enough context.
example request
Use recommend_agent_configuration to diagnose my coding agent.
Task: inspect a changing repository, edit files, run tests, and open a pull request
Environment: moderate, somewhat_predictable, medium stakes
Context relevance: long
Commitment style: balanced
Target platform: openaiResearch agent with conflicting sources
Use this when a research agent overreacts to new sources, loses earlier evidence, or sounds too confident.
example request
Use recommend_agent_configuration to diagnose my research agent.
Task: synthesize conflicting technical sources into a cautious recommendation
Environment: stable, highly_predictable, medium stakes
Context relevance: long
Commitment style: cautious
Target platform: genericWhat the output should lead with
- • TI, SG, FT, UE, AR
- • Failure-risk score
- • Predicted regime
- • Runtime posture
- • Best next test