Logic - Build and operate fleets of agents
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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.
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Logic, Inc.
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.
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?
Logic, Inc.
@avrisimon thank you! The transition is actually pretty smooth. Teams update their spec, Logic keeps the API contract stable, runs generated tests and evals, and lets you publish with approval if needed. Each version is immutable. If it regresses, you can roll back in one click and inspect the execution history to see what changed. If the new approach is better, it ships instantly without any code deploy.
It ends up feeling much more like a versioned release process than prompt editing in production. You can even use multiple versions of the agent in parallel at the same time.
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.
Logic, Inc.
@webappski Thanks, Alex! I couldn't agree more. Every LLM-based project needs fallback, as well as the ability to switch models for specific features.
Open Wearables
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.
Logic, Inc.
@piotr_pasierbek Piotr, pretty granular. You can define exact inputs, outputs, validation rules, edge cases, and processing guidelines, whether that starts as a 3 line prompt or a much longer spec. The part we care most about is what happens after authoring: we turn that spec into typed contracts, auto generate tests, block publishes when tests fail, keep immutable versions, and let you pin models when consistency matters. That gap between intent and production behavior is exactly the problem we built around, especially for high stakes workflows like healthcare.
Open Wearables
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.
Logic, Inc.
@piotreksedzik Hi Piotr - we offer both! As you mention there are two different dimensions here: performance and reliability.
Lancepilot
Logic, Inc.
@odeth_negapatan1 Thanks, Odeth. That was exactly our goal. Appreciate the shout-out!
Nex
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?
Logic, Inc.
@najmuzzaman Appreciate that. Our view is: put adaptive judgment in the spec, put deterministic actions in tools. The spec can describe how the agent should behave under different conditions, and at execution time Logic can pull in context from the knowledge library, similar past runs, or external systems before responding. So conditional logic does not have to get pushed down into tool code by default. We usually only push it into tools when you need exact side effects, strict business rules, or system-specific operations. That keeps the agent flexible without turning it into a branching workflow graph.
Agently
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?
Logic, Inc.
@ahmadhajj Thanks, Ahmad! We wrote up a detailed description of exactly that in a recent blog post: https://logic.inc/resources/logic-scores-83-3-on-ifbench-beating-every-model-on-the-public-leaderboard
Humalike
Spec-driven agents with versioned rollback is rare. How much of the IFBench gain comes from the harness vs the synthetic test generation step?
Logic, Inc.
@mcarmonas Thanks, Martí. The short answer is we have not broken the gain out into a clean harness-versus-synthetic-test percentage split. The harness gives us the reproducible execution, typed validation, versioning, and rollback layer, but the synthetic generation step is doing real work too, because it creates scenario-based tests from the spec and pushes on edge cases people usually miss. In practice the IFBench lift comes from the system working together, not one isolated trick.
There's more detail here in our recent blog post: https://logic.inc/resources/logic-scores-83-3-on-ifbench-beating-every-model-on-the-public-leaderboard
UXPin Merge
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?
Logic, Inc.
@uxpinjack The teams getting the most consistent outputs usually keep their specs very explicit: clear inputs, a strict output shape, direct decision rules, and the edge cases they care about. We’ve also found it helps to keep shared reference material in the knowledge library instead of stuffing it into every spec, then use tests to catch drift before publish. And thank you, that’s exactly the unlock we’re going after.