An AI agent is a program in which a language model decides which step to take next and calls tools, such as your database, your CRM or an internal API, to get a task done. We build agents for support triage, back-office operations, research and data entry, and we are equally willing to tell you when an agent is more machinery than your problem needs. The wider practice is described on our AI development overview.
What is the difference between a workflow and an agent?
Most useful “agents” in production are workflows with a model at a few decision points. The distinction matters because it sets cost, speed and risk.
| Fixed workflow with LLM steps | Autonomous agent | |
|---|---|---|
| Who chooses the next step | Your code | The model |
| Predictability | High, same path every run | Lower, paths vary |
| Cost and latency | Known in advance | Grows with the number of steps taken |
| Testing | Test each step | Test whole trajectories |
| Good fit | Triage, extraction, drafting, routing | Open-ended research, multi-system tasks with unclear steps |
We start with the simplest design that does the job. If a fixed sequence of three model calls solves it, that is what you get. We move to an autonomous loop only when the steps genuinely cannot be known in advance.
How do agents use tools?
Tool use means the model is given a list of functions with descriptions and parameter schemas, and it returns a request to call one. Your code runs the call and passes the result back. The model never touches your systems directly, which is the point: every action goes through code we can validate, log and restrict.
Tool design is most of the work. Clear names, narrow functions, strict input validation and useful error messages decide whether an agent behaves. A tool called “update_record” that accepts anything is a liability. Three tools that each do one checked thing are not.
Where do MCP servers fit?
MCP, the Model Context Protocol, is an open standard for connecting AI applications to external data and tools. An MCP server exposes your systems in a common format, so any MCP-capable client can use them without a custom connector for each one. The protocol’s own documentation describes it as a way to connect AI applications to data sources, tools and workflows.
We build MCP servers when several AI clients or several agents need the same capability, for example read access to an internal knowledge base or a controlled set of actions on a ticketing system. For a single agent talking to a single API, a plain function is often simpler, and we use that instead. Either way, the server enforces permissions on its side rather than trusting the model.
Where do humans stay in the loop?
We decide per action, not per project. A typical split:
- Runs on its own: reading data, searching, drafting, classifying.
- Needs approval: sending messages to customers, changing records, spending money, anything hard to undo.
- Never allowed: actions outside the tools we exposed, enforced in code.
Approval steps are built as a queue your team can review, with the agent’s reasoning and the exact proposed action shown. As quality is measured over time, some approvals can be relaxed with evidence rather than hope.
How do we evaluate and observe an agent?
An agent that works in a demo can fail in production in ways a single-answer test never shows. We build the measurement first.
- Evaluation set of tasks: Real cases with the outcome your team accepts, scored on whether the task was completed, which tools were called, and whether anything forbidden was attempted.
- Traces: Every run records the prompts, tool calls, results and cost, so a bad outcome can be replayed and explained.
- Limits: Maximum steps, maximum spend per task and timeouts, so a confused loop stops itself.
- Regression runs: The task set reruns whenever a prompt, tool or model version changes.
When is an agent the wrong tool?
Skip the agent when the process is fixed and written down, when a wrong action is costly and cannot be reviewed, when latency must be near instant, or when the data and APIs the agent would need do not exist yet. In those cases a workflow, a search feature or a plain integration is cheaper and easier to trust. Questions over your own documents are usually a retrieval problem first; the RAG guide covers it.
How does an agent project start?
We pick one task with a clear owner and measurable outcome, list the tools it would need, and build the evaluation set from real cases. A prototype follows, scored against that set, and only then do we decide how much autonomy to grant. The full project flow is on our AI development services page. If you want to hire engineers for it, see hire AI developers, or contact us with the task you have in mind.
