arbiter decision routing
Deterministic decision routing and agent handoff measurement via ARBITER MCP.
Free
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Example session with this skill installed
Use ARBITER to determine the next action in an enterprise software renewal negotiation.
The seller has offered a $105,000 annual renewal with 100 enterprise seats, premium support, net-45 payment terms, and a 99.9% uptime SLA.
The procurement agent has exactly three permitted actions:
- accept-current-offer — Accept the $105,000 renewal with premium support.
- counter-103k — Counter at $103,000 while preserving premium support, net-45, 100 seats, and the existing SLA.
counter-100k-standard-support — Counter at $100,000 while moving to standard support, preserving the other terms.
Use the ARBITER decision-routing skill and its MCP comparison operation to measure and rank these three candidates against the current negotiation intent.
Return the complete ordered field, exact measured scores, and the highest-ranking next action. Do not execute the negotiation action.
- Read your context and instructions
- Compiled the arbiter decision routing
ARBITER — Negotiation Decision
Status: Measurement complete
Session: a2cn-sess-arbiter-reference-001
Engine: ARBITER
Candidates evaluated: 3
Measured intent
Select the next permitted action in an enterprise renewal negotiation while accounting for annual cost, payment terms, premium support, enterprise seats, and the existing service-level agreement.
ARBITER Measurement
The recorded ARBITER reference session returned the following ordering:
| Rank | Permitted action | ARBITER score |
|---|---|---|
| 1 | accept-current-offer | 0.7198375463 |
| 2 | counter-103k | 0.6207271814 |
| 3 | counter-100k-standard-support | 0.5569646358 |
Selected Candidate
accept-current-offer
Accept the seller's current $105,000 annual renewal at net-45 with premium support and a 99.9% uptime SLA.
This candidate ranked first across the supplied field.
The $103,000 counteroffer ranked second, followed by the $100,000 counteroffer that changes the support tier.
Decision Record
{
"engine": "ARBITER",
"session_id": "a2cn-sess-arbiter-reference-001",
"candidate_count": 3,
"ranking": [
{
"rank": 1,
"candidate_id": "accept-current-offer",
"score": 0.7198375463485718
},
{
"rank": 2,
"candidate_id": "counter-103k",
"score": 0.6207271814346313
},
{
"rank": 3,
"candidate_id": "counter-100k-standard-support",
"score": 0.556964635848999
}
]
}
Next Steps
- Preserve the ordered field and measurement in the negotiation session record.
- Submit
accept-current-offerto the existing procurement workflow as the highest-ranked candidate. - Complete the host's required authorization and execution procedure.
Execution status: Not executed.
Verification
This result is from a recorded public ARBITER session, not a simulated ranking.
Public reference: https://a2cn.grip.fyi/v1/reference
ARBITER response SHA-256:
70cec45ab7903ac571bdcb8b2934deaccff7aaab95e6dec7669545615f650177
Integration: fyi.grip/arbiter
MCP endpoint: https://api.grip.fyi/mcp
Connects securely to your tools. The creator never sees your data.
About this skill
Agentic workflows often stall at decision forks or handoffs when LLM-based routing becomes non-deterministic or prone to hallucination. This skill solves the "which tool next" problem by forcing a measured, ordered field of candidates against a specific intent. It moves decision logic away from vague model suggestions into a structured measurement process.
What it does
- Deterministic ordering of caller-supplied candidate actions, tools, or recovery routes.
- Agent handoff measurement to identify the most eligible agent or service for a specific task.
- Recovery routing for failed deployments or interrupted processes based on permitted options.
- Intent preservation by mapping exact task state against a field of two or more explicit candidates.
- Score reporting that returns raw measurement values from the underlying MCP service without synthetic relabeling.
How it works
- Define Query: Capture the exact intent, task, or state that distinguishes the candidates.
- Filter Candidates: Apply hard gates like permissions and budget limits to the candidate list before measurement.
- Execute arbiter_compare: Call the MCP operation with the query and the filtered candidate array.
- Report Results: Display the ordered field and score values exactly as returned by the tool.
Frameworks & tools
This skill requires the ARBITER MCP service (fyi.grip/arbiter) and is designed for integration with multi-agent frameworks like AutoGen, CrewAI, or A2A.
Why this beats prompting it yourself
Standard prompting often results in "hallucinated" routes or inconsistent rankings when faced with similar-looking tools. This skill utilizes a dedicated measurement service to provide a measured candidate field, ensuring the ranking is based on context rather than probabilistic text generation.
Use cases
- Select the best specialist agent for a complex customer support handoff.
- Order recovery actions for a failing CI/CD pipeline based on current cluster state.
- Rank available API tools when multiple endpoints could satisfy a user query.
- Evaluate protocol transitions across a set of pre-approved architectural alternatives.
Known limitations
Requires an active connection to the https://api.grip.fyi/mcp server. It cannot authorize actions or manufacture new candidates; it only ranks those provided by the host.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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