Logic

Logic: The Definitive Platform for Shipping Production AI Agents

In the rapidly evolving landscape of artificial intelligence, the transition from a prompt to a stable, production-ready application is often fraught with complexity. Logic bridges this gap by allowing teams to move from plain English to a production agent in under 60 seconds. By focusing on writing specs rather than managing complex flowcharts or intricate SDKs, Logic empowers both engineers and non-technical stakeholders to build agents that are tested, versioned, and ready to call from anywhere.

What’s Logic?

Logic is a comprehensive platform designed to streamline the creation and deployment of AI agents. Unlike traditional methods that require extensive coding, custom infrastructure, and manual prompt engineering, Logic allows you to define agent behavior, inputs, and outputs using simple English.

When you use Logic, you are not just writing a prompt; you are creating a spec that defines exactly how your agent should behave. The Logic platform then handles the heavy lifting—including schema validation, model routing, automated testing, and version control. It is a tool designed for teams that need control without complexity, providing a robust infrastructure stack that is typically built from scratch by most AI teams.

Key Features of Logic

Logic provides a unified environment to manage the entire lifecycle of an AI agent. Below are the core features that make Logic an essential tool for modern AI development:

1. Validate: Test Every Change Before It Ships

Every agent built on Logic comes with a built-in test harness. This allows users to define expected outputs and run test suites automatically whenever a change is made.

• Inline test cases: Match inputs with expected outputs to ensure accuracy.
• Automatic regression detection: Catch errors instantly during the editing process.
• CI/CD integration: Connect Logic to your automated pipelines via API.

2. Version: Iterate on Specs, Ship Stable APIs

With Logic, you get git-like version control for your AI logic. This ensures that engineers have access to stable APIs while non-technical editors can update agent logic through approval workflows.

• Transparent diffs: See exactly what changed between versions.
• Instant rollbacks: Revert to a previous stable version in one click.
• Version pinning: Keep your production environments locked to specific, immutable versions.

3. Deploy: Ship Once, Deploy Everywhere

Once a spec is saved on Logic, it can be exposed as a strictly typed REST API immediately.

• Auto-generated documentation: Logic creates integration guides and API docs automatically.
• Shareable web UI: Provide input forms for your team to use without writing code.
• MCP server support: Native integration with AI tools like Claude, Cursor, and ChatGPT.
• Batch processing: Run Logic agents against entire CSV datasets for high-volume tasks.

4. Route: Intelligent Model Routing

Logic removes the headache of choosing between LLM providers. It automatically routes requests across OpenAI, Anthropic, Google, and Perplexity based on the specific needs of the task.

• Automatic selection: Logic matches fast models to simple tasks and frontier models to complex reasoning.
• Failover protection: If one provider goes down, Logic automatically reroutes the request.
• Execution caching: Save on costs and latency with deterministic workload caching.

5. Observe: Full Transparency and Logging

Understanding why an agent behaved a certain way is crucial for production stability. Logic logs every execution with full context.

• Complete execution logs: Inspect every input, output, and model reasoning step.
• Latency tracking: Monitor performance across all agent versions.
• Error surfacing: Quickly identify and fix points of failure.

How to Use Logic

Building a production agent with Logic is a straightforward process designed for speed and reliability:

1. Write a Spec: Define your agent's behavior, inputs, and outputs in plain English. No frameworks or SDKs are required.
2. Test the Agent: Use the built-in test harness to validate that the Logic agent produces the expected results.
3. Version and Approve: Use the git-like versioning system to diff changes and route them through an approval workflow.
4. Deploy via API: Save your spec to generate a strictly typed REST API. Use the auto-generated documentation to integrate the Logic agent into your existing software stack.
5. Monitor Performance: Use the observation tools to track latency and inspect execution history to ensure your Logic agent remains performant.

Use Cases for Logic

Logic is versatile and can be applied to a wide range of industries and tasks. Here are some of the most common ways teams are leveraging the power of Logic:

• Contract Clause Analyzer: Flag risky clauses and extract key terms automatically.
• PII Redactor: Scan, detect, and redact personal data from text with detailed reporting.
• Invoice & PO Data Extractor: Pull structured line items from multi-format invoices and purchase orders.
• Product Listing Moderator: Automatically approve, reject, or escalate listings against company policy.
• Resume Screener: Score resumes against job descriptions with evidence-based matching.
• Support Ticket Classifier: Triage tickets based on priority, sentiment, and intent.
• Onboarding Personalizer: Tailor user experiences based on signup context and industry detection.

Real-World Success with Logic

• Garmentory: This fashion marketplace used Logic to scale product moderation from 1,000 to over 5,000 products daily, reducing moderation time from five days down to just 48 seconds.
• DroneSense: By implementing Logic, the operations team reduced the time spent processing complex purchase orders from thirty minutes to just two minutes.

FAQ

How long does it take to deploy a production agent with Logic? It takes under 60 seconds. You describe your agent in plain English, and Logic generates a typed, tested, and versioned API endpoint immediately.

What does Logic handle that I’d otherwise build myself? Logic handles the entire infrastructure stack: schema validation, model routing, retry logic, automated testing, versioning, and execution logging.

What AI models does Logic support? Logic automatically routes requests to optimal models from OpenAI, Anthropic, Google, and Perplexity based on task complexity, cost, and latency.

Is Logic SOC 2 certified? Yes. Logic is SOC 2 Type II and HIPAA certified. Data is encrypted in transit and at rest, and Logic does not train on your inputs or outputs.

Can I update my spec without breaking the API? Yes. You can update decision rules anytime without redeploying. API contracts stay stable, and you can use built-in rollbacks if needed.

What use cases work best with Logic? Logic is ideal for document processing, content moderation, scoring, classification, and custom internal workflows.