"AI agent" has become a term applied to everything from a simple chatbot to a genuinely autonomous multi-step task-completing system, which makes it hard to know what you're actually evaluating. Here's a grounded way to think about it and get started.
A basic chatbot answers questions from a fixed knowledge base. An agent, in the more specific sense, can take multi-step actions — looking something up, making a decision based on what it found, then acting on that decision — rather than just responding to a single prompt. Relevance AI is built around this multi-step agent model specifically, rather than single-turn chat.
The most common mistake with agent tools is trying to automate an entire complex workflow on day one. Starting with one specific, well-defined task — qualifying a lead based on a few criteria, drafting a first-pass response to a common inquiry type — lets you evaluate whether the tool actually performs reliably before trusting it with anything more consequential.
AI Agent Store is useful specifically for this exploration phase — a directory of agent tools across different specializations, rather than needing to independently discover and evaluate each one. For building custom automated workflows connecting multiple tools together, Dynamiq is built for that specific orchestration layer.
Well-defined, repetitive tasks with clear success criteria — data lookup and summarization, routing based on explicit rules, drafting content from a template — are where current agent tools perform reliably. The reliability drops noticeably for open-ended judgment calls or tasks where "success" isn't easy to define in advance.
Running an agent's output past a human reviewer for the first several weeks of any new automated task — not because the tool is untrustworthy by default, but because catching its actual failure patterns early is far cheaper than discovering them after they've caused a real problem. Once you've seen enough of its output to trust the specific task, reducing that review overhead makes sense; skipping it from day one doesn't.
Many agent tools charge based on usage volume (API calls, actions taken, tokens processed), which means cost scales with how much you actually automate — a genuinely different pricing model than a flat per-seat SaaS subscription. Estimating realistic volume before committing to a plan avoids an unpleasant surprise once a pilot task scales up to real production use.
Expect the first month to be mostly evaluation and narrow-task testing, not full deployment. Teams that try to automate broadly from day one tend to have worse outcomes than ones that prove reliability on one task first, then expand — the tools themselves are capable, but trust in them needs to be built the same way trust in a new hire would be.
