Five-dimension Agent Score
Combines outcome, reliability, cost, speed, and safety into one view so teams can assess trade-offs rather than optimizing a single metric.
Latitude Agent Score
Latitude Agent Score evaluates AI agent performance using production traces across outcome, reliability, cost, speed, and safety. It helps engineering teams investigate the sessions behind a score, turn recurring failures into evaluations, and verify fixes after release.
Production scoring for AI agents
Latitude Agent Score is a production-evidence tool for evaluating AI agents. It turns traced production sessions into a score across outcome, reliability, cost, speed, and safety, helping teams understand whether an agent completed its task, failed operationally, used avoidable resources, introduced delay, or caused confirmed harm.
The score is designed as the starting point for investigation rather than a standalone benchmark. Teams can inspect the sessions behind a result, identify recurring failure patterns, convert them into evaluations, and compare behavior after shipping a fix. Latitude accepts traces through OpenTelemetry or existing trace data, with scoring gated by traffic, coverage, and confidence requirements.
Key features
Evidence-based scoring
Uses production sessions as evidence and reports a 95% confidence interval, while withholding a score when the evidence is insufficient.
Session-level investigation
Shows the sessions behind score changes and failures, allowing teams to move from an aggregate result to the underlying production behavior.
Issue-to-evaluation workflow
Helps teams identify recurring issues, turn a failure pattern into an evaluation, and monitor the same behavior in later production sessions.
Trace ingestion
Accepts production traces through OpenTelemetry or existing trace data, and can capture agent messages, costs, tool calls, and errors through Latitude telemetry.
MCP-assisted remediation
Connects evidence to an editor-based workflow through MCP so a coding agent can receive the problem context and help verify changes after release.
Practical use cases
Assess a production release
Use the five dimensions together to see whether a release improved real task completion without creating new reliability, cost, speed, or safety problems.
Triage recurring failures
Move from a score dip or recurring signal to the affected production sessions, then identify the common failure behavior instead of reviewing isolated logs.
Build regression coverage
Convert a validated production failure into an evaluation and follow it across subsequent sessions to check whether a fix remains effective.
Support an evidence-driven coding loop
Give a coding agent the relevant issue context, sample traces, and a workspace link through MCP, then verify the agent's behavior after the fix is deployed.
Pros and Cons
Pros
- Evaluates five complementary dimensions instead of treating speed or task completion as the only measure of agent quality.
- Grounds results in real production sessions and exposes the evidence behind the score.
- Includes a 95% confidence interval and explicitly withholds results when evidence is insufficient.
- Connects issue discovery to evaluations, release verification, and MCP-based coding workflows.
Cons
- A score is not available until the required traffic, coverage, confidence, and minimum-session thresholds are met.
- The benchmark depends on production traces and evidence quality; teams without suitable telemetry will need to set up tracing first.
- The supplied material describes the scoring workflow in detail but does not fully specify every supported trace source or integration.
FAQ
What does Agent Score measure?
Agent Score evaluates production sessions across five dimensions: outcome, reliability, cost, speed, and safety. It is intended to show what is affecting an agent's performance and which evidence-backed improvement to investigate next.
How do I get started with Agent Score?
Connect production sessions through OpenTelemetry or bring traces that already exist. Latitude processes the incoming evidence and checks whether the score eligibility requirements have been met.
How much production evidence is needed for a score?
A score appears only after all five dimensions pass the required traffic, coverage, and confidence gates. Latitude states that at least 1,000 eligible sessions are required, and the evidence must cover a qualifying whole-week window of 7, 14, 21, or 28 days.
How does Latitude communicate uncertainty?
Each score includes a 95% confidence interval. If the evidence is insufficient, Latitude does not display a score yet.
Is there a free plan?
Latitude is available on a free Starter plan and a paid Pro plan, with Enterprise options for custom credit volume, retention, deployment, roles, and support. Pricing and limits vary by plan.
Quick Facts
- Category
- AI agent observability and evaluation
- Primary input
- Production sessions and traces
- Scoring dimensions
- Outcome, reliability, cost, speed, and safety
- Evidence threshold
- At least 1,000 eligible sessions, subject to coverage and confidence gates
- Score window
- Shortest qualifying whole-week window of 7, 14, 21, or 28 days
- Trace standard
- OpenTelemetry supported
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