Engineering Intelligence
The Inductivee agentic AI and enterprise AI engineering blog. Deep technical posts on multi-agent orchestration, RAG pipelines, autonomous agent design patterns, and the engineering disciplines behind production agentic systems.
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Latest Articles
47 articles
OpenAI Agents SDK in Production: Enterprise Handoff Architecture for Multi-Agent Systems
OpenAI's Agents SDK — the successor to the experimental Swarm library — is now the vendor-blessed way to build multi-agent systems on OpenAI models. Handoffs, guardrails, and structured outputs are first-class primitives, and it is deliberately small enough to reason about. This is a practical look at what the SDK actually gives you, how it fits into an enterprise architecture, and the deployment patterns we use in production.
Claude Agent SDK in Production: Enterprise Patterns for Building Reliable Anthropic-Powered Agents
Anthropic renamed the Claude Code SDK to the Claude Agent SDK and, in doing so, signalled what it is really for: not just coding assistants, but a general framework for building production agents on Claude. This is a practical look at what the SDK gives you, how it compares to writing your own agent loop, and the architecture patterns worth adopting before you put it in front of real users.
Langfuse in Production: Enterprise LLM Observability Architecture for Multi-Model AI Systems
Once your LLM stack has more than one model, more than one prompt, more than one team touching it, and a bill that finance is starting to ask about, you have an observability problem. Langfuse has become the default open-source answer. This is a practical look at what LLM observability actually needs to cover, what Langfuse is and is not, and the deployment architecture we recommend for running it as the source of truth for an enterprise AI estate.
LiteLLM in Production: The Enterprise AI Gateway Pattern for Multi-Provider LLM Architecture
Once an enterprise has more than one LLM provider, more than one team consuming them, and more than one cost centre paying the bill, you have an AI gateway problem. LiteLLM has become the default open-source answer. Here is what the gateway pattern is for, what LiteLLM actually does, and the deployment architecture we recommend for running it in production.
Prompt Injection: A Defense-in-Depth Guide to Securing Enterprise LLM Applications
Prompt injection is the number-one risk on the OWASP Top 10 for LLM Applications, and it has no clean fix — instructions and untrusted data share the same token channel. Here is what prompt injection actually is, why filtering alone never closes the hole, and the defense-in-depth architecture — least privilege, output handling, guardrail tooling, dual-LLM patterns, and human-in-the-loop — we recommend for enterprise teams shipping LLM features.
vLLM in Production: Enterprise Inference Architecture for Self-Hosted LLMs
vLLM has become the default open-source serving engine for self-hosted LLMs. Here is how PagedAttention and continuous batching actually work, the deployment patterns we recommend for enterprise teams running vLLM in production, and where it fits — and does not fit — compared with TGI, TensorRT-LLM, and managed inference APIs.