FREE AI AGENT OPTIMIZATION REVIEW

Make your AI agent faster, leaner, and more reliable.

Scail analyzes how your AI agent actually executes:
across calls, tools, context transfers, retries, branches, and repeated runs,

to uncover opportunities to improve cost, latency, and reliability.

Get a Free Agent Review

No platform migration required. Start with the execution data you already have.
Patent-pending AI agent optimization technology

WHAT WE ANALYZE

Your agent may work. That doesn’t mean it runs efficiently.

AI agents can accumulate inefficiencies that are difficult to see from individual traces. Traditional observability tells you what happened. We focus on what can be improved.

Cost

Find the steps, paths, and behaviors responsible for disproportionate token and model spend.

Cost

Find the steps, paths, and behaviors responsible for disproportionate token and model spend.

Latency

Identify slow operations, repeated sequences, long execution paths, and opportunities to restructure work.

Latency

Identify slow operations, repeated sequences, long execution paths, and opportunities to restructure work.

Context

Detect unnecessary context propagation, amplification, and repeated information.

Context

Detect unnecessary context propagation, amplification, and repeated information.

Reliability

Find retries, failure concentrations, excessive iterations, and unstable execution patterns.

Reliability

Find retries, failure concentrations, excessive iterations, and unstable execution patterns.

Workflow structure

Compare runs to find recurring structures that may benefit from routing, caching, summarization, or more deterministic execution.

Workflow structure

Compare runs to find recurring structures that may benefit from routing, caching, summarization, or more deterministic execution.

How it works

Share your execution data

Provide representative traces from your agent.

Share your execution data

Provide representative traces from your agent.

We analyze behavior across runs

Optimaize normalizes execution data and analyzes aggregate behavior rather than evaluating traces only one at a time.

We analyze behavior across runs

Optimaize normalizes execution data and analyzes aggregate behavior rather than evaluating traces only one at a time.

Receive evidence-backed findings

We identify optimization opportunities and connect each recommendation to observed execution behavior.

Receive evidence-backed findings

We identify optimization opportunities and connect each recommendation to observed execution behavior.

Review the results together

We walk through findings, expected impact, implementation considerations, and the optimizations worth testing.

Review the results together

We walk through findings, expected impact, implementation considerations, and the optimizations worth testing.

What you receive

Your Agent Optimization Review includes an execution profile, major cost and latency drivers, behavioral and workflow findings, prioritized optimization opportunities, supporting trace evidence, recommended experiments, and estimated impact where the available data supports it.

It’s not a generic list of AI best practices. It’s an evidence-led view of what your particular agent should change based on how it actually runs.

Built for teams already building agents

The review is most useful for teams with an AI agent or multi-step LLM workflow already running, execution traces or logs available, meaningful model or infrastructure cost, and a need to improve performance without blindly redesigning the system. You do not need to be using a particular agent framework.

Scail Labs is building Optimaize, a platform that combines large-scale execution analysis with AI-assisted optimization techniques. Its patent-pending technology surfaces patterns that are difficult to identify by inspecting individual traces.

See what your agent could improve.

We’re currently offering a limited number of complimentary Agent Optimization Reviews as we work with teams building real-world AI agents.