Aris · Execution layer
Make agentic execution faster.
Aris is the high-performance execution layer for agentic AI.
It operates between applications and the models, tools, and compute they consume to reduce unnecessary execution, lower latency, improve throughput, and make inference more efficient.
01The first question
Does this require inference at all?
Most execution stacks assume every step goes to a model. Aris starts by asking whether it should.
The Aris advantage
Why teams running Aris spend less and respond faster.
Without Aris, every agent step goes to a model: full context, repeated work, one fixed provider. With Aris, each step is checked first. It reuses an existing result, runs as code, or goes to the right provider with only the context it needs.
Lower cost per task
Pay for inference only when it's needed
Aris removes repeated and unnecessary model calls and sends the work that remains to the right execution path, so cost tracks completed work rather than call volume.
Faster responses
Milliseconds where a model isn't needed
Reused results and deterministic steps return without waiting on inference. Latency drops sharply, especially at the tail.
Works with any stack
No rebuild required
Aris sits between your applications and the models, tools, and compute they already use. You keep your agents, frameworks, and providers.
Gains that compound
Better as workloads grow
Every completed task gives Aris more to reuse and more to learn from, so efficiency improves as volume scales instead of eroding.
Performance and economic results vary based on workload architecture, repetition, context patterns, provider mix, implementation, cache eligibility, and other technical factors.
02How Aris evaluates execution
- 01ReuseHas equivalent work already been completed?
- 02ReuseCan an existing result satisfy this request?
- 03DeterministicCan this step execute deterministically?
- 04ContextCan unnecessary context be removed?
- 05InferenceDoes this require inference at all?
- 06PathIf inference is required: what path delivers the required speed, quality, reliability, and cost?
03–08What Aris improves
Latency, throughput, inference, context, redundancy, determinism.
Latency
Faster completed work
Steps that can be reused or executed deterministically return in milliseconds instead of waiting on inference.
Throughput
More work per unit of capacity
Removing avoidable model calls frees provider rate limits and compute for work that actually needs it.
Inference efficiency
Inference where it matters
When inference is required, Aris selects the execution path that meets the required speed, quality, reliability, and cost.
Context efficiency
Only the context required
Long-running agents accumulate context. Aris reduces what is sent so each call carries what the task needs.
Redundant execution
Work is not repeated
Agents repeat work. When equivalent work has been completed, an existing result can satisfy the request.
Deterministic execution
Not every step needs a model
Validation, transformation, lookups, and rule-based steps run as code — predictable and fast.
09Economics
Cost per completed task, not cost per token.
The economic unit of agentic software is the completed task. Aris reduces the inference, context, and repeated execution behind each one — so cost scales with value, not with call volume.
10Aris vs model routing
Routing picks a model. Aris decides whether a model is needed.
Traditional model routing
- Request
- Select model
- Inference
- Result
Aris
- Request
- Determine what execution is required
- Reuse · deterministic · context optimization
- Invoke inference only where necessary
- Choose appropriate execution path
- Result
11Architecture
Where Aris sits.
Aris intercepts execution requests from applications and agents, decides the required path, and only then commits model or compute resources.
12Benchmarks
Measured results.
12×
Up to 12× lower latency
P95 · Measured on cache-eligible execution.
90%
Up to 90% lower modeled token cost
per 1,000 requests · In the measured workload.
30–45%
Redundant calls avoided
of calls reach inference · Applicable repeated execution completed without invoking inference again.
Performance and economic results vary based on workload architecture, repetition, context patterns, provider mix, implementation, cache eligibility, and other technical factors.
13Deployment options
- ManagedOperated by ContinuousAI.
- Customer VPCRuns inside your cloud account.
- HybridControl plane and execution split to your requirements.
14Security + data boundaries
Your cloud. Your credentials. Your policies. Your data boundaries.
Aris is designed to operate within customer-defined boundaries, using customer provider credentials and policies. Detailed security documentation is available on request.
15See Aris Run
Four requests. Four execution paths.
→ request summarize_ticket #4821
→ evaluate reuse? deterministic? context? inference?
→ decision REUSE
Equivalent result found · inference skipped
Illustrative simulation of Aris decision logic. Not a benchmark.
Evaluate Aris on your workload.
We'll review your execution patterns, identify where reuse, deterministic execution, and context reduction apply, and measure the result.