How We Build Agentic AI: The Plainsight SDLC
Building an AI assistant is not the same as building a chatbot. Three principles that separate practitioner-built platforms from marketing demos.
Building an AI assistant is not the same as building a chatbot
A chatbot wraps a model. An assistant orchestrates models, tools, knowledge, and permissions on your behalf. A demo of the first takes a weekend. A production version of the second takes practitioner discipline most teams underestimate.
The market is full of impressive demos that cannot survive contact with a real organisation: real users with real expectations, real data with real sensitivity, real auditors with real questions. The gap between the demo and the system is where most AI projects die.
Three principles separate the two. None of them is glamorous. All of them show up in the bill, the audit log, and the user feedback after month three.
Three principles we build with
Multi-agent orchestration
A real assistant is not one large prompt. It is a collection of specialised agents (retrieval, calendar, email, code execution, presentation building) coordinated by a router that picks the right tool for the right step. Each agent is small, testable, and replaceable. The whole becomes more capable than any single model call.
Test-driven AI development
Unit tests, integration tests, and end-to-end scenarios scored automatically by an LLM judge. We track regressions in answer quality the same way a backend team tracks regressions in latency. New behaviour does not ship if the suite goes red. This is the boring engineering hygiene that makes the difference between a demo and a system.
Operational observability
Every conversation, every tool call, every model invocation is logged with the user identity, the department, the model used, and the cost. When something looks off, the answer is in the dashboard, not in a Slack thread with the AI vendor. Buyers underrate this until the first time it matters.
What practitioner-built looks like
Two columns of signals. A short heuristic for telling whether the platform you are evaluating was designed by people who have shipped, or by people who have pitched.
Why this matters when you are buying, not building
You will not write the test suite. You will not run the orchestration layer. But you will live with the consequences of whether they were built well.
The questions worth asking a vendor are simple. How do you know the assistant is not getting worse? How do you know what it just did? How would you switch a model under it without telling anyone? If the answers are vague, the platform is younger than the marketing suggests.
The Plainsight assistant is built by a team that has shipped AI into operational environments for years. Every principle on this page came out of a real production lesson, not a slide deck.
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