Engineering Intelligence
The Inductivee Intelligence 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
54 articles
Zero Credentials in the Sandbox: What Google's Agent Compute Engine Changes About Agent Blast Radius
Most agent deployments hand the agent a long-lived, broadly-scoped credential — so compromising the runtime compromises everything that credential can reach. Google's Agent Compute Engine, announced 22 October 2026, makes a different design concrete: each agent runs in its own isolated microVM that holds no credentials. Here is the credential-scoping ladder, and where per-agent isolation actually fits on it.
Agentic AI for Supply Chain Orchestration
A supply chain runs on systems that were never built to talk to each other — ERP, planning, warehouse, transport, supplier portals. Agentic AI is best understood not as a smarter forecast but as an orchestration layer that reasons across those systems and acts in them. This is the multi-agent topology for planning, replenishment, and exception handling — and the part everyone underestimates: making the writes back into the systems of record safe.
Procurement Agents: Architecture, Controls and Approvals
Procurement agents are AI agents that handle sourcing and purchasing tasks. The hard part is control: approval bands, segregation of duties and human gates.
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.