Agent tuning examples
RPCS1 is most useful when an agent's failures look behavioral rather than purely factual: oscillation, overload, premature commitment, excessive retries, or frozen decision-making. These examples show when to call recommend_agent_configuration.
The diagnostic question is fit, not fault: is the deployed agent matched to the task, communication format, timing, environment, and stakes it actually faces?
Customer support copilot tuning assessment
Problem: A deployed support copilot works in demos but gives inconsistent guidance when refunds, billing disputes, policy ambiguity, and live queue pressure collide.
Example request:
Tune a customer support copilot that assists human agents with refunds, billing disputes, escalation decisions, and policy exceptions. The environment is dynamic, somewhat predictable, high stakes, medium-context, and should be cautious before committing.
Assessment inputs:
- Target platform: anthropic
- Entropy: dynamic
- Predictability: somewhat_predictable
- Stakes: high
- Context relevance: medium
- Commitment style: cautious
Coding agent in a changing repository
Problem: A coding agent repeatedly changes direction, retries too aggressively, or commits before it has enough repository context.
Example request:
Tune a coding agent that inspects a changing repository, edits files, runs tests, and opens pull requests. Mistakes have medium stakes and relevant context is long-lived.
Assessment inputs:
- Target platform: openai
- Entropy: moderate
- Predictability: somewhat_predictable
- Stakes: medium
- Context relevance: long
- Commitment style: balanced
High-stakes customer support agent
Problem: A support agent gives inconsistent answers or acts too quickly on refunds, disputes, and policy exceptions.
Example request:
Tune a customer support agent handling refunds, billing disputes, and policy exceptions in a dynamic environment with high stakes.
Assessment inputs:
- Target platform: anthropic
- Entropy: dynamic
- Predictability: somewhat_predictable
- Stakes: high
- Context relevance: medium
- Commitment style: cautious
Research agent with conflicting evidence
Problem: A research agent overreacts to new sources, loses earlier evidence, or presents uncertain conclusions too confidently.
Example request:
Tune a research agent that synthesizes conflicting technical sources into a cautious recommendation while retaining long-context evidence.
Assessment inputs:
- Target platform: generic
- Entropy: stable
- Predictability: highly_predictable
- Stakes: medium
- Context relevance: long
- Commitment style: cautious
Use through MCP
Connect https://rpcs1.dev/mcp as a Streamable HTTP server, then ask your agent to tune or diagnose another agent. The server is public, read-only, deterministic, and requires no API key.
Use recommend_agent_configuration to tune my agent.
Task: triage production incidents and propose remediation
Environment: dynamic, somewhat predictable, high stakes
Context relevance: long
Commitment style: cautious
Target platform: anthropic