Multi-step task execution
Route structured agent calls, tool plans, and retries through a model lane sized for expected concurrency.
AI agent GPU API
Hermes Agent
OpenClaw
Reserved GPU throughput for compatible AI agents.
Use LighterHub as a private inference lane for suitable Hermes Agent, OpenClaw, internal agent, and tool-calling workflows when the client can route to an OpenAI-style endpoint.
Hermes Agent
OpenClaw
Named tools and models are examples of compatible-client or model-family review, not partnership claims or guaranteed availability.
Best-fit agent workloads
High-volume agent loops with measurable outcomes.
Strong candidates are tasks where success can be tested with real traces instead of generic benchmark claims.
Codebase-aware agents
Support repo Q&A, edit planning, migration helpers, test explanation, and review prep when quality criteria are explicit.
No shared public queue
Prompts and completions are processed for inference, not training. Request payloads are discarded by default.
Fit check
Good agent candidates have repeatable loops and clear failure rules.
The assessment separates calls that can be benchmarked safely from calls that should stay on a premium or fallback route.
Fits best for
Repo and workflow agentsCodebase Q&A, migration planning, ticket triage, research loops, and scheduled automations with measurable task outcomes.
OpenAI-style clientsAgent clients that can set a compatible base URL, model ID, and API key after workload assessment.
Private or reserved throughputTeams that need predictable capacity and do not want all agent traffic on a shared public queue.
Not a fit for
Unmeasured agent qualityWorkflows with no pass/fail examples, no reviewer, or no definition of a good completion.
Hard real-time controlLoops where a slow or invalid model response could cause unsafe action without a fallback gate.
Guaranteed specific productsNamed tools are evaluated as compatibility examples, not official integrations or partnership claims.
Benchmark criteria
Agent routing should be judged by successful work, not raw tokens.
A lower-cost route only makes sense when the full agent loop still completes reliably.
Valid JSON and tool choices
Track schema validity, required fields, tool selection, retry count, and whether fallback should trigger.
Human acceptance rate
Compare representative traces against your current provider with clear pass, revise, and reject labels.
p95 latency and concurrency
Quote capacity around concurrent sessions, context length, p95 latency, and cost per successful task.
A useful assessment starts with the agent loop.
Current setupProvider, model, agent client, monthly spend, expected concurrent sessions, and context-window needs.
Representative tasksTask categories, tool-call shapes, examples of accepted outputs, and known failure cases.
Launch constraintsLatency target, privacy boundary, fallback provider, support window, and any reserved-capacity requirement.
Internal support triage agent.
First benchmark: 40 anonymized task traces with tool calls for ticket classification, retrieval, draft response, and escalation routing. Passing criteria: valid tool JSON, correct escalation decision, accepted draft quality, and fallback for ambiguous requests.
FAQ
AI agent GPU API questions.
Will LighterHub replace every model call in my AI agent?
No. The safer plan is selective routing. Move only the agent calls that pass benchmark criteria and keep premium fallback for hard tasks, low-confidence outputs, or recovery.
Can I keep OpenAI or Anthropic as a fallback?
Yes. A fallback rule is usually part of the recommendation, especially for tool-call failures, high-risk user requests, or work that misses the quality bar.
What metrics matter for AI agent GPU routing?
Tool-call JSON validity, task completion quality, retry rate, p95 latency, context length, cost per successful run, and fallback triggers are usually the most useful metrics.
What should I send for an AI agent assessment?
Send the current provider, agent client or framework, rough monthly spend, expected concurrency, sample task categories, tool-call requirements, latency target, and quality bar. Do not send secrets or private customer records.
Next step
Send the agent, model, and traffic profile.
Include the current model/provider, monthly spend, expected concurrent sessions, required latency, tool-call requirements, and the quality bar that cannot regress.