PRACTICAL AI FIELD GUIDES
Guides for building useful, reliable AI experiences
Explore practical articles on AI agents, RAG, integrations, security, and operations. Browse by topic or search for the question you need answered.
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AI Agents
Design useful agents, choose the right level of autonomy, and plan safe human handoffs.
RAG & Data
Make business knowledge searchable with sound retrieval, evaluation, and data practices.
Integrations
Connect assistants to websites, APIs, CRMs, and the tools your team already uses.
Security & Privacy
Set trust boundaries, protect customer data, and design safer AI workflows.
Industry & Applications
See how AI assistants fit real customer and operational workflows.
Evaluation & Operations
Measure quality, monitor production behaviour, and keep improving over time.
Latest articles
Explore new ideas and practical guidance from the ChatNexus library.
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What Does an AI Agent Implementation Actually Include?
A practical breakdown of the workflow, data, tools, permissions, evaluation, human handoffs, and operating work required for a production AI agent. Read guide
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AI Agent Vendor Scorecard: Compare Platforms on What Matters
A vendor scorecard helps you look past a polished agent demo and compare the things that determine whether the workflow will survive production. Read guide
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RAG vs Fine-Tuning for Business Knowledge: Which Should You Use?
RAG keeps changing knowledge current, while fine-tuning changes repeatable behaviour. The right choice depends on which problem is costing you time. Read guide
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Event-Driven AI Agents: Webhooks, Queues and Safe Actions
Event-driven agents become dependable when the event is verified, the work is queued, duplicate deliveries are harmless, and people can see what happens next. Read guide
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AI Agent Kill Switches and Rollbacks: Safe Recovery
A recovery plan should let you stop one unsafe workflow, understand what already happened, and resume safely instead of shutting down everything. Read guide
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Human Approval Patterns for AI Agents: Review the Right Boundaries
Human approval is most useful when a person is asked to judge one clear, consequential action with the evidence and context needed to take responsibility for it. Read guide
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Least Privilege for AI Agents: Tools, Data and Tenants
Least privilege lets an agent help with a real job without giving it a master key. Start with the person, the task, and the smallest useful capability. Read guide
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AI Agent Threat Modeling: Trust Boundaries, Tools and Recovery
Threat modelling for an agent can start with a simple story: what can it see, what can it change, and what happens if someone gives it misleading instructions? Read guide
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AI Agent Observability: Trace Tasks, Tools and Outcomes
When an agent goes wrong, observability should let you answer what it saw, what it did, and where the workflow changed course. Read guide
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Structured Outputs for AI Agents: Schemas Are Not Safety
Structured output makes an agent easier to connect to software, but tidy JSON does not make a decision true or safe. Read guide
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Context Engineering for AI Agents: Give the Right Information
Context engineering is the everyday discipline of giving an agent the right background for one task without burying it in irrelevant or unsafe text. Read guide
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AI Agent Memory: What to Store, Retrieve and Forget
Agent memory should save people effort without turning guesses, stale facts, and private conversations into permanent truth. Read guide
2 min read