Architecture: Latent Execution for Agentic Workflows
How CLC enables multi-step AI workflows to avoid repeated recomputation instead of replaying context — safely.
What CLC Is
CLC is execution-layer infrastructure for multi-step AI workflows.
Modern agentic workflows repeat the same context across steps. Each step re-processes shared information—documents, conversation history, prior reasoning—even when nothing has changed. This causes unnecessary cost and latency that scales with workflow depth.
CLC solves this at the execution layer by enabling reuse of prior computation. Workflow steps can avoid repeated recomputation instead of replaying text. Agent logic and models remain unchanged. Only execution changes.
CLC is not a model, not an agent framework, and not an API wrapper. It is infrastructure that sits between agent orchestration and inference runtimes, enabling predictable execution across sequential steps.
Where CLC Sits
CLC lives inside the execution layer, between orchestration and inference runtimes.
Application / Agent Logic
Planner, executor, verifier, tools — unchanged.
Agent Orchestration
Existing frameworks or custom logic.
Latent Execution (LE)
Enables predictable execution across steps.
CLC lives here.
Inference Runtime
The underlying inference runtime.
The system changes how execution happens, not what inference produces. Agent behavior stays the same. Inference behavior stays the same. Only execution changes.
Canonical Latent Communication
The internal mechanism that enables workflow steps to avoid repeated recomputation.
Canonical Latent Communication (CLC) is the internal mechanism that allows workflow steps to avoid repeated recomputation instead of replaying text. It is not a standalone product, not an open standard, and not a protocol specification.
CLC is a capability delivered through execution. It enables the execution layer to maintain continuity across workflow steps while preserving correctness guarantees.
Latent Execution (LE) — The Product
The execution layer that enables predictable reuse of prior computation.
Latent Execution is the execution layer that enables predictable reuse of prior computation during inference. It provides the guarantees required for production use.
Key Capabilities
- Maintains step boundaries
- Ensures compatibility between steps
- Handles continuation safely
- Provides guarantees in production
Evaluation vs Production
LE-0 enables evaluation. LE enables dependency.
| Capability | LE-0 — Free Evaluation Mode | LE — Production Latent Execution |
|---|---|---|
| Workflow Shape | Fixed multi-step workflow shape | Flexible workflow configuration |
| Configuration | No configuration | Full execution visibility |
| Guarantees | No guarantees | Enforced execution guarantees |
| Safety Guarantees | Not available | Safety and correctness guarantees |
| Observability | No observability | Observability and auditability |
| License & Support | Evaluation only | Commercial license and support |
LE-0 enables evaluation. It provides a fixed workflow shape with no configuration, no guarantees, and no observability. It is intended for testing and benchmarking only.
LE enables dependency. It provides execution guarantees, safety and correctness guarantees, observability and auditability, and commercial license and support. LE is required for production use.
What This Enables
Outcomes, not features.
Lower inference cost
Deep workflows no longer multiply cost with each step. Cost scales with new work, not repeated context.
Predictable latency
Latency stabilizes as step count increases. No exponential growth from reprocessing shared context.
Agent depth without economic blowup
Workflows can deepen without hitting cost walls. Verification loops become viable by default.
No changes required
Existing agents and models run unchanged. Only execution changes, not behavior or outputs.
What This Is Not
Not a model
CLC does not change inference capabilities or reasoning behavior.
Not an agent framework
CLC does not replace orchestration frameworks or agent logic. It works alongside them.
Not request-level optimization
CLC operates at the execution layer, not through request-level optimization or prompt optimization.
Not prompt optimization
CLC does not modify prompts, instructions, or prompt engineering techniques.
CLC makes latent reuse possible.
Latent Execution makes it reliable.