Shipping a real AI agent can mean weeks of wiring up prompts, retries, eval harnesses, and logging before you see production. Logic solves that. You write a structured spec that describes what the agent should do, and Logic gives you a fully managed agent, with evals, observability, model routing and more built in, ready to be called from anywhere.
When you build an AI agent, the call to the LLM API is the easy part. The hard parts are evals, RAG, observability, prompt refinement, model selection, fallback, cost and latency tuning, system integrations, and giving the agent tools to do useful work in the rest of the world.
Logic gives you an out-of-the-box answer for all of that, while also improving how reliably your agents follow instructions.
With Logic, you write a simple spec that explains what the agent should do. We give you back a managed agent that can be called via MCP, REST, a web UI, or a dedicated email address. We generate well-typed schemas and synthetic tests, handle versioning, observability, and RAG, and give your agents a "batteries included" tool suite:
Real-World Capabilities: All Logic agents can read 130+ document formats, fill out PDF forms, semantically search your knowledge library, send and receive email, do research, generate and annotate images, and call HTTP APIs.
Smart Model Routing: Route across OpenAI, Anthropic, Google, and hardware-accelerated open-source models, with fallback and cost/latency tuning, so you can improve reliability without being locked into one provider.
Deep Integrations: Easily connect to external tools like Linear, Notion, and any MCP endpoint.
We make your agents smarter.
When Logic's agent harness was measured against Allen AI's IFBench, one of the hardest public tests for precise instruction following, Logic scored 83.3% – higher than any model on the Artificial Analysis leaderboard. This is a six-point gain for the agent harness above the same base model (Gemini 3.1 Pro) when called directly.
So far, 250+ organizations have automated over 4M agentic tasks with Logic. Common use cases include things like content moderation, document parsing, data extraction, medical coding, and user onboarding.
Logic is SOC 2 Type II and HIPAA certified, there's a free tier, and paid plans that scale with usage.
Jess, my co-founder and CTO, and I will be in the comments. We're excited to see what you build with it, and we'd love to hear what else you wish it could do.
This is a big unlock for teams shipping agents. Writing a spec instead of stitching together prompts, retries, and eval harnesses sounds like a huge time saver. Any plans for letting teams share or remix specs across orgs?
The SOC2 HIPAA angle is important if this is truly targeting enterprise adoption.
Looks awesome. Getting an agent to actually work in production is a whole different challenge vs vibe coding automations, and this feels like it removes a lot of that headache. Excited to try this as a PM.
Really like this direction. Turning plain English specs into production-ready agents is a big unlock. How are teams typically structuring their specs to keep outputs consistent?
Spec-driven agents with versioned rollback is rare. How much of the IFBench gain comes from the harness vs the synthetic test generation step?
the 6 point gain on IFBench over the base model is pretty impressive. what's actually happening in the harness that improves instruction following that much?
The structured spec approach is a smart bet. Most agent frameworks right now ask you to wire everything imperatively, which makes it really hard to reason about what the agent is supposed to do versus what it actually does. Curious how the spec handles cases where an agent needs to adapt its behavior based on context it did not have at definition time. Is there a way to express conditional logic in the spec, or does that get pushed into tool implementations?
routing across multiple providers is a strong feature. vender lock-in has been a real concern lately, so this helps reduce that risk.
Deep integrations + smart model routing = agents that actually work in production, not just demos. Great job Logic team!
model routing caught my attention. are you handling fallbacks automatically when one model is down, or is it more about cost/performance optimization? seems like a huge operational headache to manage manually across different providers.
the structured spec approach is interesting - how granular can you get with the agent behavior definitions? we've been building healthcare agents and the biggest pain is always the gap between "here's what it should do" and actually getting consistent behavior in production.
multi-provider routing with fallback is usually the thing that gets rebuilt from scratch on every project. either you're locked into one provider or you've added a custom routing layer on top of whatever sdk you started with. having it in the agent harness directly is the right place for it.
The spec-driven approach is the right abstraction here. When I was CTO scaling from 15 to 120 engineers, the biggest pain with internal AI tooling wasn't the LLM call itself - it was everything around it: eval harnesses that nobody maintained, prompt versions scattered across repos, and zero observability into why an agent started failing on Tuesday. The fact that Logic handles model routing, versioning, and evals out of the box means teams can skip the 3-month infrastructure detour and actually ship. Curious how you handle spec evolution - when a team realizes their agent needs a fundamentally different approach mid-production, how smooth is the transition between spec versions?
About Logic on Product Hunt
“Build and operate fleets of agents”
Logic launched on Product Hunt on April 27th, 2026 and earned 283 upvotes and 26 comments, earning #3 Product of the Day. Shipping a real AI agent can mean weeks of wiring up prompts, retries, eval harnesses, and logging before you see production. Logic solves that. You write a structured spec that describes what the agent should do, and Logic gives you a fully managed agent, with evals, observability, model routing and more built in, ready to be called from anywhere.
Logic was featured in Productivity (650.6k followers), Developer Tools (511.7k followers) and Artificial Intelligence (467.2k followers) on Product Hunt. Together, these topics include over 285.8k products, making this a competitive space to launch in.
Who hunted Logic?
Logic was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Logic stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ben Lang
Hey Product Hunt. I'm Steve, co-founder of Logic.
When you build an AI agent, the call to the LLM API is the easy part. The hard parts are evals, RAG, observability, prompt refinement, model selection, fallback, cost and latency tuning, system integrations, and giving the agent tools to do useful work in the rest of the world.
Logic gives you an out-of-the-box answer for all of that, while also improving how reliably your agents follow instructions.
With Logic, you write a simple spec that explains what the agent should do. We give you back a managed agent that can be called via MCP, REST, a web UI, or a dedicated email address. We generate well-typed schemas and synthetic tests, handle versioning, observability, and RAG, and give your agents a "batteries included" tool suite:
Real-World Capabilities: All Logic agents can read 130+ document formats, fill out PDF forms, semantically search your knowledge library, send and receive email, do research, generate and annotate images, and call HTTP APIs.
Smart Model Routing: Route across OpenAI, Anthropic, Google, and hardware-accelerated open-source models, with fallback and cost/latency tuning, so you can improve reliability without being locked into one provider.
Deep Integrations: Easily connect to external tools like Linear, Notion, and any MCP endpoint.
We make your agents smarter.
When Logic's agent harness was measured against Allen AI's IFBench, one of the hardest public tests for precise instruction following, Logic scored 83.3% – higher than any model on the Artificial Analysis leaderboard. This is a six-point gain for the agent harness above the same base model (Gemini 3.1 Pro) when called directly.
So far, 250+ organizations have automated over 4M agentic tasks with Logic. Common use cases include things like content moderation, document parsing, data extraction, medical coding, and user onboarding.
Logic is SOC 2 Type II and HIPAA certified, there's a free tier, and paid plans that scale with usage.
Jess, my co-founder and CTO, and I will be in the comments. We're excited to see what you build with it, and we'd love to hear what else you wish it could do.
Thanks for taking a look.