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Plain-English explainers on AI agents, agentic systems, and custom software — what they are, how they work, and how to build them right.

◇ Explainer

AI agent architecture

The building blocks of a production AI agent — reasoning core, tools, memory, the control loop, and the safety layer — and how they fit together into a system you can trust.

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How to evaluate an AI agent

How to tell whether an AI agent actually works — defining success, building a test set, scoring the whole trajectory not just the answer, and evaluating continuously in production.

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AI agent governance

How organisations stay in control of autonomous agents — the policies, permissions, accountability, and audit trails that make an acting AI system safe and defensible.

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AI agent observability and monitoring

How to see what an AI agent is doing — tracing every step, logging decisions and tool calls, watching for drift, and knowing when something has gone wrong before users do.

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AI agent use cases for business

A plain-language tour of where AI agents earn their keep in a real business — the categories of work they handle well, concrete examples in each, and how to tell a strong use case from a weak one.

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A plain-language explanation of AI data privacy and security — what happens to your data when an AI system processes it, the risks unique to language models and agents, and the controls that keep both safe.

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The custom software development process

A plain-language walk through how custom software gets built — from discovery and design through development, testing, and launch — and what each stage produces so you know what you are paying for at every step.

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Generative AI vs agentic AI

A plain-language comparison of generative AI and agentic AI — what each one does, how they relate, and why the difference matters when you decide what kind of system to build.

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How AI agents handle exceptions

A plain-language explanation of how AI agents deal with the unexpected — the inputs, failures, and ambiguous cases that fall outside the happy path — and why exception handling is what makes an agent trustworthy.

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How to build an AI agent

A practitioner walkthrough of building an AI agent — from defining the goal and wiring tools to adding memory, setting the authority bar, and evaluating before production.

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Human-in-the-loop AI explained

What human-in-the-loop means for AI agents — keeping a person on the decisions that carry real cost, why it matters, and how to design the handoff so it actually works.

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LLM orchestration explained

A plain-language explanation of LLM orchestration — the layer that coordinates models, prompts, tools, memory, and control flow so a language model becomes a reliable, multi-step system instead of a single chat call.

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Multi-agent systems explained

How multiple AI agents work together — when splitting work across specialised agents helps, the common coordination patterns, and why one good agent often beats five.

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Tool calling and function calling explained

A plain-language explanation of tool calling and function calling — the mechanism that lets a language model reach beyond text to fetch data and take actions in real systems, and the thing that makes agents possible.

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Voice AI agents, explained

A plain-language explanation of voice AI agents — software that listens, understands, reasons, and speaks in real time over a phone line or app, taking real actions instead of reading a script.

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What is a system of record?

A plain-language explanation of a system of record — the authoritative source of truth for a given set of business data, the one place every other system and every AI agent must trust and defer to.

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What is agentic AI?

Agentic AI is the shift from AI that answers to AI that acts — systems that plan, decide, and execute multi-step work toward a goal, with tools and a degree of autonomy.

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What is an AI agent?

A plain-language explanation of AI agents — software that perceives a goal, decides on actions, uses tools, and acts in a loop until the work is done, not just answers a prompt.

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What is an AI SDR?

A plain-language explanation of an AI SDR — an AI agent that does the sales-development work of researching prospects, reaching out, qualifying responses, and booking meetings, within rules a human sets.

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What is MCP (Model Context Protocol)?

A plain explanation of the Model Context Protocol — the open standard that lets AI agents connect to tools and data through one consistent interface instead of bespoke integrations.

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What is RAG (retrieval-augmented generation)?

A clear explanation of retrieval-augmented generation — the technique that grounds a language model in your own documents by retrieving relevant passages before it answers.

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What is workflow automation?

A plain-language explanation of workflow automation — software that runs a sequence of business steps from end to end on a defined trigger, so a process moves itself instead of waiting on a person to push each stage.

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