Context switching overhead
Long contexts thrash caches built for short inference bursts—not hours of task changes.
Agent workload benchmark lab
AgentCore Silicon models the memory, context, tool-call, and concurrency demands of long-running AI agents.
GPUs are great at inference. Agents also need memory, coordination, context recovery, and tool-call throughput.
Wafer topology
Agent workload lanes · live pressure
Benchmark the cost of agents that remember, switch tasks, call tools, and run for hours—not a single forward pass.
Long contexts thrash caches built for short inference bursts—not hours of task changes.
Agents need retained state across steps. GPUs optimize tensor residency, not agent session graphs.
Serial tool hops amplify tail latency. Throughput charts hide per-hop scheduling tax.
Independent streams contend for the same memory hierarchy without agent-aware scheduling.
Sustained runs accumulate fragmentation and checkpoint stalls that batch jobs rarely see.
Checkpoint/replay for agents is not the same as deterministic kernel relaunch.
Dial in agents, context, tools, concurrency, and reliability—then record a run to the lab dashboard.
Wafer topology
Agent workload lanes · live pressure
Agent efficiency
Architecture comparison
Tool-call pipeline
24 calls/task · ingress + serialize + API + egress
Live preview updates locally. Saving a run stores it in the demo database for the benchmark lab dashboard.
Context lanes
Pinned lanes for hot context with lane-aware eviction.
Memory persistence
Hardware-backed agent state tiers from ephemeral to durable.
Tool-call acceleration
Dedicated ingress/egress for API-shaped traffic patterns.
Agent scheduler
Fairness across agents with latency-budget aware preemption.
Concurrency fabric
Mesh between tool pipelines, memory, and execution tiles.
State recovery
Fast resume paths for multi-hour runs without cold restarts.
GPU baseline vs agent-optimized architecture for a representative lab profile.
Architecture comparison
Representative projection
Every saved run opens the benchmark lab: efficiency score, concurrency graph, cost per agent-hour, and chip profile.
Agent efficiency
91
Context switching overhead
22%
Projected gain vs GPU
2.8x
Memory bottleneck risk
Low
Tool-call latency drop
38%
Cost / agent-hour cut
44%
Production agents combine long-context models, tool use, memory, and automation workflows. Infrastructure cost pressure is pushing teams to measure agent-hours—not tokens alone. AgentCore Silicon is aimed at that measurement gap.
Not by default. AgentCore targets agent-shaped bottlenecks—memory persistence, tool hops, coordination—often alongside GPUs for model execution.
Treat numbers as directional projections from your workload knobs. They surface where GPU-style stacks pay coordination tax versus agent-native silicon.
A composite of projected scheduling efficiency, tool latency headroom, and memory pressure for your chosen agents, context, and reliability bar.
No. The lab runs entirely in-browser and on your own benchmark records stored locally in this demo database.
Run the benchmark lab, inspect concurrency and memory pressure, then talk with our silicon team.