Over the past year, "AI agents" has become one of the most talked-about phrases in software — and also one of the most misunderstood. Most businesses picture a better chatbot. In reality, it's a fundamentally different category of tool: a system that isn't given a question, but a goal — and it works out how to reach it on its own. At INTERFASE we build these solutions for real-world production use, so we've set out what an agent actually is, where it makes sense, and what to watch out for.
A chatbot answers. An agent acts.
A classic chatbot works in a single step: it receives an input and generates an output. An agent works in a loop — it breaks the task down into steps, carries them out using tools, continuously evaluates the result, and repeats until it reaches the goal. The difference is a bit like the one between a calculator and an accountant: both can crunch numbers, but only one of them knows what needs to happen next.
| Chatbot | AI agent | |
|---|---|---|
| Input | A question | A goal / task |
| Number of steps | One | As many as needed |
| Access to tools | No | Yes |
| Memory | Within the conversation | Long-term |
| Output | Text | A completed task |
Anatomy of an agent
A language model (LLM) on its own is just a text generator. It becomes an agent once you wrap it in four layers:
- Planning. The model breaks the task down into a sequence of steps and decides the order. The clearer the task and the context, the more reliable the plan.
- Tools. Through defined interfaces, the agent calls APIs, reads from databases, runs code, and sends emails. Tools are what give it real reach beyond text.
- Memory. Short-term memory holds the context of the current task, while long-term memory (a knowledge base, history) lets it pick up where it left off and learn from the past.
- The loop. The think → act → check the result → continue cycle runs until the goal is met or it hits a defined boundary.
This loop is exactly what distinguishes an agent from a one-off answer — and it's also what needs the closest watching.
The frameworks powering agents
Building an agent "from scratch" means solving a lot of tedious but critical plumbing: reliable tool calling, memory management, retries on failure, safety boundaries. Specialised frameworks are increasingly taking over this layer. Two we consider worth watching:
- openclaw — an orchestration framework for long-running agents. It focuses on reliable tool calling and robust long-term memory, so the agent retains context across many steps and tasks. It suits cases where the agent isn't producing a single answer but accompanying a longer process.
- hermes — emphasises structured, predictable output and deterministic workflows. It's the right fit where you need the agent to stick to an exact format and sequence of steps rather than improvise — in integrations and processes where "creativity" is more of a risk than an asset.
Choosing a framework isn't a matter of dogma. What decides it is the nature of the task: a long autonomous process versus a precise, repeatable workflow.
Where agents deliver real value
They have the biggest impact where decision-making over data repeats and where people currently lose time switching between systems:
- Customer support. The agent looks up an order, checks its status in the internal system, and replies with real context — not a generic stock phrase.
- Document processing. It extracts structured data from invoices, contracts, or emails and writes it into the right system, including checks and flagging unclear cases.
- Internal operations. Routine tasks across your software — reports, syncs, alerts — run without a person having to trigger them.
Based on what we've seen, a well-deployed agent can take over a substantial share of the routine steps in a given process (estimated time saved, %):
These numbers aren't a promise — they're the order of magnitude we see in projects. The real benefit always depends on the specific process, data quality, and the scope of automation.
It doesn't work without guardrails
An agent with access to your systems is a powerful tool and a potential risk. That's why we approach it with the same discipline as any production software:
- Scope of permissions. The agent is allowed to do only what it strictly needs — nothing more.
- Human-in-the-loop. A human makes the call on sensitive or irreversible steps.
- Logging. Every decision and tool call is recorded and traceable.
- Gradual rollout. We start narrow and expand scope only once the agent reliably handles the smaller slice.
A good agent isn't one that can do everything. It's one that reliably does a single thing well — and one you can trust to do it.
Conclusion
AI agents are shifting software from a tool you use to an assistant that acts on your behalf. Frameworks like openclaw and hermes handle the technical foundation, but success ultimately comes down to three things: quality data, precisely defined tools, and clear boundaries. If you're weighing up where an agent would make sense in your business — and where it wouldn't — get in touch with us. We're happy to walk through it with you in detail.