Reasoning-based AI Agentic Workflow Platform

Closing the Loop on
Enterprise AI Logic.

Transform scattered data into rigorous, autonomous reasoning cycles.
Build self-correcting AI agents that think in loops, not lines.

Build Your Logic Loop Explore Features
0% Logic Accuracy
0ms Avg Cycle Time
0M+ Cycles Processed
Core Capabilities

The Recursive Intelligence

Three pillars that define the next generation of enterprise AI reasoning.

Self-Correcting Logic

AI identifies logical errors in real-time and automatically enters repair cycles. No human intervention needed for routine corrections.

Logical Grounding

Recursive Reasoning

Simulates deep human thinking through multi-round iterations. Each cycle refines the output, converging on optimal solutions for complex tasks.

Continuous Evolution

Deterministic Output

Establish deterministic business logic rules within stochastic AI models. Predictable, auditable, and enterprise-ready results every cycle.

Governance Ready
Product Features

Engineered for Reasoning

A complete platform for building, orchestrating, and governing AI logic workflows.

Input RAG Context
Reason Chain-of-Thought
Decide Logic Gate
Output Verified
Self-Correction Cycle
01

The Logic Engine

Build complex decision chains using RAG and long-context techniques. The Logic Engine decomposes problems into reasoning steps, validates each inference, and produces traceable conclusions.

  • Multi-step chain-of-thought reasoning
  • RAG-powered contextual grounding
  • Automatic logic validation at each step
  • Full inference trace for auditability
IF
score > 0.8
TRUE
THEN
Approve
FALSE
LOOP
Re-evaluate
02

Cycle Orchestrator

A visual drag-and-drop interface for defining agent loop logic. Map out If/Then/Loop patterns, set convergence criteria, and watch your agents reason through complex workflows.

  • Visual drag-and-drop workflow builder
  • If / Then / Loop logic primitives
  • Convergence criteria & exit conditions
  • Real-time execution visualization
12:04:32.001 Logic gate passed — confidence 0.94
12:04:32.047 Cycle iteration #3 — drift detected
12:04:32.089 Self-correction applied — re-grounded
12:04:32.112 Output verified — deterministic ✓
03

Governance & Audit

Monitor every step of logic execution. Full audit trails, drift detection, and compliance reporting ensure your AI reasoning is safe, transparent, and controllable.

  • Step-by-step execution audit trail
  • Logic drift detection & alerting
  • Compliance & regulatory reporting
  • Role-based access controls
Developer Ecosystem

Built for Builders

SDKs, APIs, and integrations for Python, .NET, and more. Ship reasoning agents in minutes.

logic_agent.py 
from logiccycle import Agent, LogicEngine, Cycle

# Initialize the reasoning engine
engine = LogicEngine(
    model="gpt-4-turbo",
    grounding="rag",
    max_iterations=5
)

# Define a self-correcting logic cycle
cycle = Cycle(
    name="contract-review",
    convergence_threshold=0.95,
    exit_on="deterministic"
)

# Build and run the agent
agent = Agent(engine=engine, cycle=cycle)
result = agent.run(
    task="Analyze contract clauses for risk",
    context=documents
)

print(result.trace)  # Full reasoning trace
print(result.output)  # Deterministic output

SDKs & APIs

Native SDKs for Python, .NET, TypeScript, and Go. RESTful APIs with OpenAPI specs.

Interactive Playground

Test logic cycles in real-time. Visualize reasoning traces and iterate on agent behavior.

Vector DB Integration

Built-in connectors for Milvus, Pinecone, and Weaviate for contextual recall.

Open Source Core

Core logic primitives are open source. Extend, customize, and contribute on GitHub.

Logic Diagnostic

See the Cycle in Action

Enter a task and watch how LogicCycle AI reasons through it step by step.

Cycle 1 — Context Retrieval
Querying vector store for relevant context... Grounding with 12 document chunks.
Cycle 2 — Reasoning
Applying chain-of-thought decomposition... Identified 4 logical branches.
Cycle 3 — Validation
Logic gate check: confidence 0.87 < 0.95 threshold. Entering correction loop...
Cycle 4 — Self-Correction
Re-grounding with additional context. Revised confidence: 0.96. Threshold met ✓
Output — Deterministic Result
3 high-risk clauses identified. Reasoning trace attached. All logic gates passed.

Ready to Close the Loop?

Start building reasoning agents that think in cycles, not lines. Join the next generation of enterprise AI.

Build Your Logic Loop Talk to Sales