Building autonomous AI agents that handle real business workflows

What agents actually are, when they make sense, and how to architect them for production.

AI agents are LLM-powered systems that can take sequences of actions — calling tools, making decisions, handling exceptions — to complete a goal, with human approvals and controls where they matter.

The hype around agents is real. The implementations that work in production are rarer.

What an agent actually is

An agent is a loop: the model receives a goal and context, decides what action to take (call a function, retrieve data, write something, pass to a human), executes that action, observes the result, and decides the next action. This continues until the goal is met or the agent determines it can't proceed.

The key capability is that the model can handle branching — when step 3 fails, it can try a different path rather than stopping.

When agents make sense

Agents are the right architecture when the workflow has genuine branching — when the right next step depends on what happened in the previous step. If your workflow is linear, a simple pipeline is more reliable and easier to debug.

Good agent use cases: customer support triage that needs to pull order data, check policies, and draft responses based on what it finds. Document processing that extracts data, validates it against rules, and routes exceptions. Research workflows that search, synthesize, and format findings.

What breaks in production

The failure modes are predictable. Models get confused about what tools are available. Long contexts cause the model to lose track of goals. Tool calls fail and the model doesn't handle errors gracefully. The agent loops indefinitely.

The fix is defensive architecture: explicit tool descriptions, error handling baked into every tool, maximum step limits, human-in-the-loop escape valves, and extensive logging.

Our approach

We build agents for production, not demos. That means exhaustive error handling, structured outputs, deterministic steps where possible, and fallback to human review when the agent is uncertain. The goal is a system that handles 90% of cases autonomously and escalates the rest gracefully.

If you're thinking about agents for a specific workflow, book a strategy call. We'll tell you whether it makes sense.