Designing Agentic Workflows That Actually Work

Every failed automation project we've seen had one thing in common: the team started coding before they understood the workflow. Agentic systems are unforgiving — a misunderstood edge case at step 3 can cascade into a complete breakdown by step 8.

Start with the Human Workflow

Before an agent can automate a task, you need to map exactly how a human does it today. We sit with operators and document every micro-decision: What do you check first? What happens if X is missing? Who do you escalate to?

This produces what we call a decision map — every branch, every exception, every dead end. This is your ground truth.

Identify Agent Roles Early

Not every step needs an AI agent. Some steps are deterministic and belong in a rule engine. Others require language understanding and belong to an LLM. A common mistake is over-agentifying — giving an AI agent a task that a simple if-statement could handle reliably at 10× the speed.

We classify each step as:

• Deterministic → rule / function
• Classification → small classifier or prompt
• Generation → LLM agent
• Human → keep it human

Test with Edge Cases First

Don't test happy paths. Feed your agents the weirdest, most broken inputs you can imagine. Real production data is messy. An agent that handles 95% of cases beautifully but catastrophically fails on the other 5% is not production-ready.

The 80/20 Principle for Automation

Aim to automate the 80% of cases that are routine. Design a clean handoff for the 20% that aren't. A human-in-the-loop for edge cases is not a failure — it's good engineering.