Multi-Model Orchestration
Policy-aware model routing, bounded retries, and controlled failover. Route each task by capability, cost, and latency without silently crossing data boundaries.
provider adaptersrouting policiesretry budgets
INDEPENDENT AI RESEARCH & DEVELOPMENT
Autonomous agents.
Autonomous agents.
Engineered
for control.
Next-generation agent infrastructure for enterprise engineering. We’re developing context-aware workflows, multi-model orchestration, and test-verified code generation—with explicit control at every step.
Research preview. Register interest in future beta access.
cagan / orchestration.py
PREVIEW 0.1# Proposed workflow configuration
# Illustration only — no API call
import os
workflow = {
"provider": "anthropic",
"model": os.getenv("ANTHROPIC_MODEL"),
"context": "repository.retrieve",
"agents": ["planner", "engineer", "reviewer"],
"tools": {"sandbox": True, "max_steps": 12},
"approval": "required_before_write",
"verification": ["schema", "tests", "policy"],
} LOCAL SIMULATION · NO API REQUESTS
PROPOSED INTERFACE · PROVIDER ADAPTER CONCEPT
Intelligence, orchestrated.
Model-agnostic by design Context-first workflows Human approval boundaries
01 / Core infrastructureBeyond prompts.
Beyond prompts.
Built around systems.
Our research centers on four infrastructure layers for reliable agent execution. Capabilities below describe the platform’s development direction.
Deep Context Memory
Vector retrieval and RAG pipelines that bring relevant repository knowledge into context. Track source provenance, freshness, and access permissions.
hybrid retrievalscoped memoryprovenance
Deterministic Tool Calling
Typed contracts, explicit state transitions, and isolated execution. Deterministic control flow around probabilistic models—not a promise of identical generated code.
schema validationsandboxesidempotency
Enterprise Guardrails
Research into tenant isolation, least-privilege tools, security filters, and auditable execution. Privacy and retention controls are explicit design requirements.
policy gatesaudit eventsdata controls
02 / Reference architectureEvery action.
Every action.
An explicit boundary.
01 / CONTEXT
Retrieve what matters
Permission-scoped knowledge with traceable sources.
02 / PLAN
Define the workflow
Typed objectives, tool budgets, and approval checkpoints.
03 / EXECUTE
Act within bounds
Isolated tools with scoped credentials and bounded retries.
04 / VERIFY
Validate the outcome
Tests, policy checks, and a reviewable execution record.
CROSS-CUTTING CONTROLS / authorization · audit events · human approval
Design target
99.98%
Control-plane availability
Target monthly availability for future hosted orchestration.
Design target
<120 ms
P95 routing overhead
Excludes model inference, retrieval, and tool execution.
Design objective
Zero.
Default prompt-content retention
Provider policies and operational metadata require separate controls.
These are engineering targets, not measured benchmarks, service guarantees, or current retention commitments. Results will require published workloads, test conditions, and provider-specific validation.
03 / Engineering use cases
From context to consequence.
Workflows we’re exploring with a focus on inspectable outputs and controlled execution.
Repository engineering
Explore codebases, propose scoped changes, and evaluate patches against tests before human review.
Technical knowledge retrieval
Connect specifications, documentation, and implementation details with source-linked context.
Multi-agent coordination
Separate planning, implementation, and verification into bounded roles with explicit handoffs.
Build with intentDeploy Autonomous Systems
Deploy Autonomous Systems
with Cagan Labs
Help shape infrastructure for the next generation of engineering workflows. Register your interest in private beta access.