How to Ship LLM Agents: Comparing Your Infrastructure Options
Shipping an AI agent seems to be straightforward enough to start: pick a framework, wire up the LLM API, and get the feature into production. Most teams scope the integration work at days, maybe a couple of weeks. The API call itself is rarely the bottleneck, but the infrastructure around it is where projects stall.
Prompt management, testing, version control, model routing, and error handling all need to exist before an agent is production-ready, and none of it ships with the API. Teams that budget for the integration but not the infrastructure discover the gap mid-project, after timelines are set and commitments are made. Whether you build that stack yourself or offload it determines whether agents ship this quarter or next.
Most teams evaluate several paths: building the infrastructure from scratch, adopting LangChain, LangGraph, or CrewAI for orchestration, or offloading to Logic. Each trades control for speed differently, and each leaves specific work to you.
Custom Development
Custom development gives you complete control over every layer of the stack: no vendor dependencies, no abstraction layers to debug through, no constraints on architecture. For teams where the AI processing itself is the product, that control matters.
The scope of production infrastructure is where estimates break down. Beyond the core agent behavior, teams end up building:
Rate limiting, retry logic, and error handling
Multi-provider routing with failover across GPT, Claude, and Gemini
Versioning for prompts and configurations
Testing and evaluation frameworks
Observability, logging, and debugging tools
Schema validation and type safety
Basic versions of these components can ship relatively quickly using existing libraries and patterns. But teams building full-scale AI platforms with deployment orchestration, compliance frameworks, and security often find the work stretches across multiple weeks with a dedicated team, pulling engineers away from the core product roadmap.
The maintenance burden compounds the initial investment. Connector development, access rights management, audit logs, and provider updates all require ongoing attention. Every engineer maintaining agent infrastructure is an engineer not shipping product features, and that opportunity cost grows quarter over quarter.
Custom development is the right path when your team has requirements no existing tool supports, or compliance mandates that prevent third-party platforms.
Logic
Logic takes a spec-driven approach: you write a natural language spec describing what you want an agent to do, and Logic generates a production agent with the infrastructure already included. When you create an agent, 25+ processes execute automatically: research, validation, schema generation, test creation, and routing optimization. You can have a working proof of concept in minutes and ship to production the same day.
The production infrastructure that teams typically build themselves ships with every Logic agent:
Typed REST APIs with auto-generated schemas
Auto-generated tests that validate changes before deployment
Version control with instant rollback
Execution logging for debugging without guesswork
Multi-model routing across GPT, Claude, and Gemini
Structured output schemas are also generated automatically from your spec, so you don't maintain those manually as your agent evolves. When requirements change, you update the spec and the agent behavior updates instantly, while your API contract remains stable. Domain experts can update rules if you choose to let them, with every change versioned and testable using guardrails you define.
{{ LOGIC_WORKFLOW: extract-structured-resume-application-data | Extract and transform structured application data }}
What Logic leaves to you: defining what your agents should do, and handling domain-specific validation outside the agent itself. Multi-step agent-to-agent orchestration and self-hosted deployment are not yet available.
Garmentory's content moderation demonstrates what this looks like in production. The platform processes 5,000+ products daily across 190,000+ monthly executions. Review time dropped from 7 days to 48 seconds, error rates fell from 24% to 2%, and the contractor review team went from 4 to 0. DroneSense saw similar results with purchase order processing: validation dropped from 30+ minutes to 2 minutes per document (93% reduction), with no custom ML pipelines or model training required.
LangChain / LangGraph
LangChain provides multi-provider abstractions, an extensive integration library, and a large open-source ecosystem. For prototyping, it gets a working demo running very quickly. LangGraph extends that foundation with graph-based state management for workflows requiring complex conditional logic and state transitions.
Where LangChain excels at orchestration, it leaves production infrastructure to you. Prompt management, testing, version control, error handling, and deployment pipelines are separate concerns that teams build on top of the open-source framework.
LangGraph's graph model gives you explicit control over how agents move between states, which matters for workflows where you need to audit or explain agent decisions. That control comes with upfront design work: defining state schemas, managing checkpointers, and handling graph cycles requires understanding LangGraph's execution model before writing business logic. When requirements change, you're updating node dependencies, reworking execution paths, and retesting graph traversals alongside the business rules themselves.
Both tools solve real orchestration problems. The question is whether your team has capacity to bridge the demo-to-production gap while maintaining product velocity.
CrewAI
CrewAI organizes agents into role-based teams that collaborate through sequential delegation. The "team of specialists" model maps intuitively to how many engineering teams already think about dividing work, making it one of the more accessible entry points for multi-agent development.
The framework fits workflows where distinct responsibilities hand off sequentially: research feeds into drafting, drafting feeds into review. CrewAI also offers a managed cloud platform for teams who want to reduce some operational overhead beyond what the open-source framework provides.
The role-based model works best when workflows map cleanly to defined handoffs. When agents need to backtrack based on intermediate results or handle responsibilities that shift based on context, rigid role definitions can create friction. Each role boundary becomes a potential failure point requiring its own error handling, and each handoff needs validation to ensure the previous agent completed its work correctly.
As with LangChain, the framework provides the orchestration layer. Testing, version control, and deployment infrastructure remain your team's responsibility.
Choosing the Right Path
When AI processing is the product itself, custom development is worth the investment. When AI enables a feature, LLM document extraction, content moderation, classification, scoring, the infrastructure underneath it doesn't differentiate your product. Most teams significantly underestimate the work required to make LLM integrations production-ready, and every week spent on that infrastructure is a week not spent on features customers pay for.
The real alternative to Logic is building that infrastructure yourself, which means engineering time on prompt management, testing harnesses, deployment pipelines, and ongoing maintenance rather than your core product. Logic ships the production layer with every agent: typed APIs, auto-generated tests, version control with instant rollback, multi-model routing, and execution logging. The platform processes 250,000+ jobs monthly with 99.999% uptime over the last 90 days, backed by SOC 2 Type II certification with HIPAA available on Enterprise tier.
It comes down to whether your team's engineering capacity is better spent on agent infrastructure or on the features your customers are paying for. Start building with Logic.
Frequently Asked Questions
Why does the gap between a working prototype and production deployment take so long?
The LLM integration itself is usually straightforward. The gap comes from everything around it: prompt management systems to track what's running in production, testing infrastructure that catches regressions before users do, version control for rolling back when updates cause failures, error handling for API timeouts and malformed responses, and model routing when different tasks need different providers. Each layer is a separate engineering project, and most teams don't scope them until the prototype is already working and the timeline is already set.
When does custom development make sense over offloading infrastructure?
Custom development tends to make sense when the AI system itself is the differentiated product and the team expects to iterate deeply on model behavior, routing, and internal tooling over time. It is also common when compliance or data residency requirements prohibit third-party platforms. In those cases, teams should budget not only for initial build-out but also for ongoing ownership: monitoring, upgrades, provider changes, and the operational burden that accumulates after the first release.
What production infrastructure do teams typically need beyond LangChain or CrewAI?
LangChain and CrewAI handle how agents chain calls, manage state, and coordinate steps. The production surface area around them includes deployment pipelines, testing harnesses that catch regressions, prompt and version management, execution visibility for debugging, and consistent error handling across model providers. How much of this teams build themselves versus offload is the core infrastructure decision.
How does Logic's spec-driven approach differ from LangChain or CrewAI?
LangChain and CrewAI provide orchestration primitives that teams build production infrastructure on top of: deployment pipelines, testing, version control, and error handling are separate engineering work. Logic inverts that model. You describe what the agent should do in a natural language spec, and Logic generates a production API with typed schemas, auto-generated tests, versioning, and model routing already built in. The tradeoff is less granular control over orchestration internals in exchange for shipping to production faster.
Can teams start with LangChain or CrewAI and migrate to Logic later?
Yes. Logic generates standard REST APIs, so teams can run it alongside existing framework implementations during transition. A common pattern is offloading one agent to Logic while keeping others on an existing stack, then expanding based on results. The API-first architecture means integrations don't need to change when the underlying infrastructure does.