AI agents, explained.

An AI agent is software that can take actions on your behalf — not just answer questions, but actually do things. It can browse the web, send emails, update a spreadsheet, trigger a workflow, or book a meeting. The key difference from a chatbot: an agent doesn't stop at generating text. It uses tools to act in the world. You describe what you want done; the agent figures out the steps and executes them.

ChatGPT gives you answers. An AI agent takes actions. ChatGPT can tell you how to draft a follow-up email to a prospect; an AI agent can draft it, find their contact details, send it, and log the activity in your CRM — without you touching a keyboard. The underlying language model is often the same (GPT-4, Claude, etc.). What's different is the layer on top: the harness that connects the model to your tools, your data, and your workflows.

The use cases working well right now: sales follow-up sequences, customer support triage, internal document Q&A, meeting prep and follow-up notes, data extraction and spreadsheet updates, and routine ops tasks that currently require a human to click through a workflow. The use cases still rough: anything requiring fine judgment calls, client-facing conversations with emotional stakes, or real-time decisions that need to be right the first time.

A harness is the framework that connects an AI model to tools, memory, and the outside world. The model (Claude, GPT-4, etc.) is the brain — it understands language and reasons about what to do. The harness is the body — it gives the model hands: the ability to browse the web, read files, run code, send messages, and remember past interactions. OpenClaw, Hermes, and Paperclip are harnesses. Lindy and Relevance AI are SaaS products built on top of harness technology.

Open-source harnesses (OpenClaw, Hermes, Paperclip) run on your own hardware, cost nothing beyond API fees, and give you full control over your data and infrastructure. The tradeoff: you need someone technical to set them up and maintain them. SaaS platforms (Lindy, Relevance AI) are managed for you — no servers, no maintenance, ready in an afternoon. The tradeoffs: monthly subscription costs, your data lives on their infrastructure, and you're limited to what the platform allows.

It depends on which platform you choose. Lindy and Relevance AI are genuinely no-code — non-technical operators can build useful agents without writing a line of code. n8n and Stack AI are low-code — a smart non-developer can configure them, but the initial setup usually benefits from a developer for an hour or two. Open-source harnesses (OpenClaw, Hermes, Paperclip) require a developer or a technically confident operator who's comfortable with a terminal.

There are two cost layers to budget for. The platform cost: SaaS platforms run $20–$200+/month. Open-source harnesses are free software you run yourself. The model cost: every time the agent calls the underlying AI (Claude, GPT-4, etc.) you pay per token. A light-use agent might run $5–$20/month in API costs; a busy agent handling hundreds of tasks a day can reach $100–$500/month in model costs alone. Build your estimate around both layers.

It depends on the deployment model. Open-source harnesses running on your own servers never send data to a third party beyond the AI model API you've chosen. SaaS platforms process and store your data on their infrastructure — check their data processing agreements, especially for anything involving customer PII or proprietary business data. For regulated industries (healthcare, finance, legal), verify SOC 2 compliance and data residency requirements before deploying anything customer-facing.

Anything requiring consistent fine judgment. AI agents still make errors, take wrong turns, and occasionally hallucinate facts — especially in novel situations. Don't deploy them unsupervised for: client communications with significant stakes, financial decisions without a human review step, or anything where a mistake would be irreversible. The pattern that works: AI agents as force multipliers for a human, not autonomous replacements. Use them to handle the first 80% of a task; keep a human in the loop for the final call.

No technical resources, need it working this week: start with Lindy. Have one developer and want maximum flexibility: n8n. Want a self-hosted personal AI that controls your computer: OpenClaw. Want an agent that improves over time and you're technical: Hermes. Running multiple agents already and need governance: Paperclip. Not sure? Use our Agent Picker — five questions, one recommendation.

Multi-agent orchestration is the practice of coordinating multiple AI agents toward a shared goal — each handling a different part of a workflow. One agent might research a prospect, a second draft the email, a third schedule the send. Orchestration platforms like Paperclip manage the division of labor: assigning tasks, enforcing budgets, requiring approvals for high-stakes actions, and maintaining an audit trail of every decision. If you're running two or more agents that need to coordinate, you need an orchestration layer.

RPA tools (UiPath, Automation Anywhere) follow rigid, pre-defined scripts. They're brittle — change the layout of a web page and the script breaks. AI agents reason about what they're looking at and adapt. An AI agent can read an invoice in any format, not just the specific layout you scripted for. The tradeoff: RPA is more predictable and auditable; AI agents are more flexible but can surprise you. For structured, stable workflows, RPA is still solid. For anything that varies in format or requires judgment, AI agents have largely replaced it.