AGENT TRACE ANALYSIS
Tracing tells you what happened. Optimaize analyzes traces to determine what should change. Modern AI agents generate detailed execution traces containing model calls, tool activity, retrieval, context, timing, token usage, errors, retries, and control flow.
Trace data → Execution analysis → Behavior patterns → Optimization findings → Evidence-backed recommendations → Validation
An agent trace is a record of how an AI agent executed a task: model and tool calls, retrieval operations, inputs and outputs, token usage, cost, latency, retries, errors, workflow transitions, loop iterations, and evaluation results. A trace gives visibility into one execution. Agent trace analysis looks across execution data to understand recurring behavior: where the agent consistently spends resources, which operations add latency, whether context travels farther than necessary, and where workflows could be simplified.
Tracing systems show what the model received and returned, which tools were called, how long operations took, how many tokens were used, how much model calls cost, where errors occurred, and how the workflow progressed. That answers what happened. Analysis adds another layer: whether an expensive operation is necessary, whether a long-running tool is critical, whether a repeated call is useful exploration or unnecessary duplication, and what should change.
Individual traces can be noisy. Agents may take different paths based on task, model decisions, retrieved information, tool responses, and intermediate results. Agent trace analysis looks for behavior that persists across runs: a workflow step that dominates cost, a repeated context pattern, operations that frequently occur together, paths that are substantially slower, retries clustered around one operation, or workflows that iterate more than expected. These patterns provide stronger evidence for optimization decisions than isolated observations.
Finding cost inefficiencies
Trace analysis asks why an execution cost what it did. Cost can accumulate through repeated model calls, excessive context, repeated retrieval, unnecessary tool activity, long paths, retries, iteration, and expensive operations repeated across stages.
Finding latency bottlenecks
Timeline views show where an agent spends time, but end-to-end latency can also come from execution structure: many reasonable operations run sequentially, repeated acquisition, retries, or paths that simply perform more work.
Understanding context through traces
Retrieved information, tool outputs, intermediate results, conversation history, and model outputs can be carried forward. Analysis identifies where context appears disproportionate to the work being performed and asks what information each step actually needs.
Analyzing tools and dynamic execution
Tool use is a major source of cost, latency, and complexity. Analysis shows which tools dominate time, where repeated acquisition occurs, which paths create unusual tool activity, and where high iteration counts or failures and retries concentrate.
Optimization recommendations should be connected to the execution behavior that motivated them. A useful recommendation is not simply “reduce token usage.” It shows where unnecessary usage occurs, why that area is a meaningful opportunity, and how a proposed engineering change could improve the agent. The same applies to latency, tool activity, workflow behavior, and reliability.
An optimization opportunity is not proof that a change will improve the system. Reducing context may lower cost but affect output quality; changing behavior may improve latency but reduce reliability. Proposed improvements should be evaluated against cost, token usage, latency, reliability, and quality to determine which ideas result in measurable gains.
Optimaize turns agent execution traces into evidence-backed optimization opportunities across cost, latency, context, tool activity, workflow behavior, and reliability. Rather than requiring developers to inspect large numbers of traces manually, it looks across executions for recurring patterns that may indicate inefficient behavior. Those findings provide the evidence behind recommendations that can then be evaluated while preserving the quality and reliability the application requires.
Every production execution reveals which paths your agent takes, where it spends time and tokens, how it uses context, which tools it calls, and where it repeats work or struggles. Tracing captures that evidence. Optimaize helps turn it into engineering decisions.
Analyze your agent traces