We Deployed an AI Agent to Production. The Hard Part Wasn't the AI.
Deploying an AI agent for a client taught us more about organisational challenges than about the technology itself. The first agent we deployed functioned flawlessly in staging. Once in production, it began making decisions that were technically correct but operationally disastrous. No one had defined the boundaries of what it could do autonomously. This led us to deeply reconsider our approach to governance and oversight.
Concrete Experience of Worksdem with an AI Agent
Initially, project expectations were high: the AI agent was anticipated to improve efficiency and streamline operations. However, the real challenges we faced weren't technological but organisational. We witnessed teams getting stuck for weeks on prompt engineering while completely neglecting logging. An agent that leaves no human-readable trace isn't a tool—it's a risk.
One of the principal lessons we've learned is that the question we pose to clients before discussing AI agents isn't 'which model do you use', but 'who is accountable when the agent does something unexpected?' If there's no immediate answer, we're not ready. We've realised that we don't trust fully autonomous AI agents in enterprise contexts. We trust agents with explicit human checkpoints. The difference isn't philosophical—it's the difference between a system that scales and one that quietly fails.
A client approached us to 'add an AI agent to their workflow'. After analysis, we told them their workflow wasn't structured enough to be automated by a human, let alone an agent. We started from there.
Lessons Learned on Operational Management and Governance
We've learned that the real issue with AI agents in production isn't model accuracy. It's that nobody has delineated the rules for what happens when the agent halts midway through a critical process. This drove us to develop specific strategies to tackle operational challenges.
Clear operational boundaries and ongoing human oversight have become central elements in our approach. Despite advanced technology, governance and supervision remain crucial.
Comparison with Industry Standard Practices
In the broader industry context, many organisations often underestimate the human aspects and process management surrounding AI more than the technology itself. This comparison has helped us position our experience and make clear that the real challenges lie more in management and role definition than in technology.