Description
Summary AI Tools and Agents Researcher We're looking for a researcher with a strong working knowledge of the AI and automation landscape to build two things: a structured library covering the major tools and platforms, and a set of profiles of the AI agents businesses are actually paying to have built. This is research and writing rather than implementation. You won't be building workflows or shipping anything. You will be working out what each tool genuinely does, where it stops being the right answer, and what the recurring agent patterns look like in practice. Most of what's published on both subjects is either vendor marketing or someone rewriting vendor marketing. The value here is in getting past that. 1. Tool library A profile for each major tool and platform, all answering the same set of questions: 1. What is it? 2. What are its main capabilities, and what can it actually do? 3. What is it best for, and what types of workflows or business problems does it suit? 4. When should a consultant choose it, and when should they reach for something else? 5. What systems can it connect to, and what APIs and integrations does it support? 6. What are its limitations? 7. How hard is it to implement? 8. What does it typically cost? 9. What are the main alternatives? Tools we'd expect to see covered, though the final list should come out of your research rather than this one: * Workflow automation: n8n, Make, Zapier, Microsoft Power Automate * Models and AI platforms: ChatGPT and OpenAI, Anthropic and Claude, Gemini * Business systems: Salesforce, HubSpot, Xero * Categories rather than single products: RPA platforms, AI document processing, AI search and knowledge tools, voice AI platforms, agent frameworks Alongside the profiles we want a capability matrix. Where the profiles are organised by tool, this is organised by capability, so someone can start from a need like "extract data from supplier invoices" and see their options. For each capability, set out which tools can do it, what each one actually does in practice, the use cases it fits best, where it falls short, and what the alternatives are. Between the two, a reader should be able to understand what each tool does and when it's appropriate to use it. 2. Business AI agents Research the 20 most common and commercially useful AI agents currently being built for businesses. We want the agents companies actually pay to have built, not a list of speculative ideas. For each one, document: * What it is, and what the agent actually does * Why it gets built, and the business problem behind it * When it's appropriate, and what makes a company a good candidate for it * How it's built, including the typical components, tools and systems involved * What it uses: models, APIs, databases, business systems and anything else *