Most AI products spend tokens deciding what each message needs — before
the first token of actual work. ConductorOS eliminates that cost entirely.
workflow: name: route-on-complexity routing: type: declarative route: # Zero token overhead — this is metadata simple: SLM # < 128 tokens, factual reasoning: LLM # logic, code, multi-step creative: LLM # writing, ideation
Dynamic routers run a classifier on every prompt. That classifier itself calls a model — costing tokens before the first real answer is generated.
You can’t see where a message goes until after it goes there. Debugging means watching logs and guessing, not reading a workflow definition.
When routing logic lives in code, it changes silently between deploys. Rollbacks mean git archaeology and fragile migration scripts.
More AI usage means more routing decisions. At scale, the overhead becomes the dominant cost line — invisible, compounding, never audited.
ConductorOS uses declarative routing — the workflow topology is fixed at definition time, not discovered at runtime. No LLM in the orchestration loop. No tokens spent deciding what runs next.
Write a YAML file that defines your agents, their models, inputs, outputs, and routing logic. It’s a config file, not code. Diff it in a pull request. Version it with your codebase.
agents:
- id: triage
model: arcee-blitz # fast, cheap
prompt: Classify complexity
- id: deep-think
model: virtuoso-large
prompt: Reason through this
routing:
rules:
- condition: "input.tokens < 64"
target: triage
- condition: "input.type == 'reasoning'"
target: deep-think
See the full routing graph before anything executes. Every decision path is visible in advance — which model handles which input, how conditions branch, where human gates sit.
The orchestration layer consumes zero tokens. Your prompts go directly to the right model, every time. Scale to billions of executions without watching your routing bill grow faster than your inference bill.
ConductorOS separates orchestration from execution. The routing graph is a JSON/YAML file — deterministic, auditable, source-controlled. Your workers are plain code in any language. No SDK ritual, no determinism constraints.
YAML-defined workflow topology. Fixed at definition, not discovered at runtime. Route decisions cost zero tokens.
Routing decisions resolve in under 1ms. No classifier model to invoke. Your latency budget stays intact.
OpenAI-compatible endpoint. Swap providers without rewriting workflows. Per-agent model overrides.
Approval gates and review checkpoints built into the workflow graph. Not bolted on later — part of the declaration.
Every task executes to completion, or every failure is explicitly handled. State persists. Restarts pick up where you left off.
Workers in Python, Go, JavaScript, Java, C#, Ruby, or Rust. No framework rules. Plain code, any library.
Routing is infrastructure, not intelligence. The moment your orchestration layer starts making decisions for you — instead of executing decisions you’ve already made — you’ve built a second AI system inside your AI system. You pay for it twice.
ConductorOS is the operating system for AI cost optimization. Define your routing once. Run it forever. Pay only for the work that matters.