The Unicorn blog
AI, agents and automation, explained plainly
Practical articles from our engineers on what works in AI today — and what to watch out for.
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- Agentic AIModel Context Protocol (MCP) explained for business leadersThe Model Context Protocol (MCP) is an open standard, introduced by Anthropic in 2024, for connecting AI models to tools and data. Instead of a custom integration for every model and system, you expose a system once as an MCP server and any compatible AI application can use it. 2 min readRead
- AI strategyA responsible AI checklist for growing businessesResponsible AI for a growing business comes down to a few habits: know what data each AI system uses, tell people when they are dealing with AI, keep a human accountable for important decisions, test before launch, and keep records. Regulations such as the EU AI Act are making these habits expected rather than optional. 2 min readRead
- AI strategyAgentic AI vs RPA: what changes for business automationRobotic process automation (RPA) follows fixed, scripted steps and breaks when inputs change. AI agents handle variation — unstructured emails, documents and requests — by reasoning about what to do. The strongest setups use agents for judgement and keep scripted automation for stable, high-volume steps. 2 min readRead
- Agentic AIAI agent vs chatbot: what is the difference?A chatbot answers questions; an AI agent completes tasks. An agent reads and updates your business systems, runs several steps in sequence, checks its own work and asks a person before risky actions. 2 min readRead
- AI strategyHow to choose your first AI agent use casePick a task that happens often, follows clear rules, uses systems with an API and is cheap to correct if the agent gets it wrong. Prove value there before moving to harder work. 2 min readRead
- AI strategyHow to measure the ROI of an AI agentMeasure an AI agent the way you would measure a new hire: record how the task is done today, run the agent on the same work, and compare time per task, error rate, cost and the share of tasks it completes without help. Include the cost of human review in the comparison. 2 min readRead
- Agentic AIMulti-agent systems: when one AI agent is not enoughA multi-agent system splits a large task across several specialised AI agents, usually coordinated by a supervisor agent that plans the work and checks results. It suits long, multi-step work; for simple tasks a single well-built agent is easier to run and debug. 2 min readRead
- AI securityPrompt injection explained: the security risk every AI app must handlePrompt injection is when text an AI system reads — an email, a web page, a document — contains instructions that try to override what the system was told to do. It cannot be fully prevented by prompting alone, so safe AI systems limit what a manipulated model could actually do. 2 min readRead
- AI fundamentalsRAG vs fine-tuning: which does your business need?Use retrieval-augmented generation (RAG) when answers depend on facts in your documents that change over time. Use fine-tuning when you need the model to adopt a style, format or narrow skill. Many production systems use RAG first and add fine-tuning only if needed. 2 min readRead
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