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
51 articles
Context Engineering: The Discipline That Replaced Prompt Engineering
Prompt engineering was the discipline of the last cycle. Context engineering is the discipline of this one. It is the recognition that what an LLM does is decided far more by what you put in its context window — the retrieved documents, the memory, the tool outputs, the system instructions, the order they appear in — than by the exact wording of the prompt itself. This is a practical look at what context engineering actually means as a body of technique, why it emerged, and how enterprise teams should organise around it.
Voice AI Agents in Production: Enterprise Architecture for Real-Time Voice-First Systems
Voice AI agents are the first genuinely new interface pattern in a decade — real-time, natural, and now finally responsive enough to feel like a conversation rather than a phone tree. The technology is here. The enterprise architecture that makes them reliable, observable, and safe is not obvious. This is a practical look at the components a voice agent stack actually needs, the vendor decisions that matter, and the deployment patterns we use when we build voice-first systems for enterprise customers.
DSPy in Production: Programming, Not Prompting, the Enterprise LLM Stack
Prompt engineering — the discipline of hand-tuning strings until a model does what you want — is what enterprise LLM systems currently run on, and it does not scale. DSPy is Stanford's answer: treat the LLM as a compilable target, describe what you want in code, and let a compiler optimise the prompts against your data. This is a practical look at what DSPy is, what it changes about how you build and maintain LLM applications, and the enterprise architecture patterns we recommend for teams adopting it.
Pydantic AI in Production: Type-Safe Enterprise Architecture for Reliable LLM Applications
Pydantic AI takes the type-safety and validation discipline that made Pydantic itself the default of the Python data-modelling world and applies it to agents. For enterprise teams that have been burnt by hallucinated JSON, silently-drifting output shapes, and free-text agent responses that break every downstream system, that is the exact discipline missing from most agent stacks. This is a practical look at what Pydantic AI is, what it changes about the agent-build cost curve, and the architecture patterns we use when we deploy it.
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.