Logic

What is Logic?

Logic is a spec-driven agent platform for turning plain-English specifications into production-ready AI agents. It focuses on the end-to-end workflow of getting from defined behavior to callable APIs, including evaluation, versioning, and operational visibility.

The core idea is that what you write as an agent spec defines its inputs and outputs. Logic then generates the tooling needed to test changes, manage versions, expose the agent via an API, and route requests to models.

Key Features

• Spec-based agent definitions (inputs/outputs): Define behavior in a spec so the agent’s interface and expected results are derived from your written definition.
• Built-in test harness with inline expected outputs: Add test cases directly to the agent definition and run them on save to catch regressions.
• Automated regression detection and CI/CD integration via API: Run evaluation in automated pipelines instead of relying only on manual testing.
• Versioning, diffs, and rollback: Every spec change is versioned and diffed, with reversibility and version pinning to keep APIs stable.
• Approval workflows for non-technical spec editors: Use approvals to let non-engineers update agent logic without redeploying or managing prompts in a separate tool.
• Typed REST API generation and auto-generated docs/UI: Save an agent spec to generate a strictly typed API, documentation, and a shareable web UI with input forms.
• PII redaction with reporting: Detect and redact personal data from text while producing detailed reports on what was found.
• Policy evaluation and moderation decisions: Evaluate product listings against policies to approve, reject, or escalate items.
• Intelligent model routing with failover and caching: Logic routes requests across OpenAI, Anthropic, Google, and Perplexity based on task complexity; it also provides automatic failover and execution caching for deterministic workloads.
• Execution logging for debugging: Log full context for each call (inputs/outputs, model reasoning, latency) across agent versions.

How to Use Logic

1. Write or choose an agent spec (including defining expected inputs and outputs).
2. Add inline test cases with expected outputs, then run the test suite on save to validate behavior.
3. Iterate on the spec with versioning, diffing, and approval workflows as needed.
4. Once ready, save to generate a strictly typed REST API, API documentation, and a shareable web UI.
5. Use execution logs to inspect what happened for specific calls and to diagnose errors with context and latency.

Use Cases

• Contract clause review: Use a “Contract Clause Analyzer” spec to flag risky clauses and extract key terms from contracts.
• Record matching across systems: Apply an “Entity Matcher” spec to match records with a confidence score.
• Invoice and purchase-order extraction: Use an “Invoice & PO Data Extractor” to parse multi-format documents and extract structured line items.
• Personal data handling for text workflows: Run a “PII Redactor” to detect and redact personal data while producing a report of detected entities.
• Product listing moderation: Use a “Product Listing Moderator” to compare listings against policy rules and decide whether to approve, reject, or escalate.

FAQ

Does Logic require engineers to redeploy for spec changes?

The site describes versioning and approval workflows so engineers can keep stable APIs while agent logic is updated through approvals, without redeploying or managing prompts in a separate tool.

What happens when a provider fails or errors?

Logic includes automatic failover if a provider returns errors or goes down, and it also routes requests by task complexity, latency, and cost.

How does Logic help verify agent behavior before release?

Each agent includes a built-in test harness: inline test cases with expected output matching run on save, with regression detection designed to catch regressions; CI/CD integration is available via API.

Can I generate both an API and a UI from the agent spec?

Yes. Logic generates a strictly typed REST API, auto-generated API docs, and a shareable web UI with input forms based on the saved spec.

What visibility is available after deployment?

Logic logs full execution context per API call, including inputs, outputs, model reasoning, latency, error surfacing, and execution history across agent versions.

Alternatives

• No/low-code agent builders with built-in prompts and evaluations: These can help non-developers assemble agents, but may rely less on spec-driven versioning and typed API generation from a written definition.
• Prompt evaluation tools and custom test harnesses: You can combine separate prompt testing, logging, and CI steps, but this typically requires more integration work than using a unified platform.
• Open-source orchestration frameworks: You can build routing, logging, and evaluation yourself, but it shifts the burden of infrastructure and operational tooling onto your team.
• Custom API/front-end stacks: If you build your own typed REST endpoints and UI, you may gain flexibility, but you’ll need to implement versioning, testing workflows, and execution observability manually.