Agentic Bridge Framework: Closing the Gap Between Agentic Capability and Performance Benchmarks

While agentic AI systems perform impressively on emerging capability benchmarks, existing performance evaluation suites focus on non-agentic workloads, leaving a critical gap in understanding system efficiency for multi-step, tool-using agents. We present the Agentic Bridge Framework for extracting actionable performance insights from capability evaluations through trace-level telemetry. Applying this framework to a multi-agent system on GAIA validation, we reveal that: (1) pass@N strategies provide diminishing accuracy returns; (2) search agents dominate token usage and latency, identifying web data gathering as the primary bottleneck; (3) reasoning models spend more tokens on context preservation than actual reasoning, highlighting costly inter-agent communication overhead. These findings inform critical design choices—context engineering, tool-use optimization, and phase-aware resource allocation—and illustrate how agent traces can inform reproducible performance workloads, bridging capability achievements with systems optimization for efficient agentic AI.