We Built Something We Can't See
Last month I shipped an automation for a logistics client in Medellín that routes delivery agents based on real-time traffic data. It works. The system makes decisions, adjusts routes, occasionally overrides manual input. On paper, it's autonomous.
But here's what keeps me up: I have no idea what happens when it fails in a way I didn't predict. Not because the code is bad. Because I outsourced the decision-making to something that has no stake in the outcome. The delivery drivers do. The customers do. The agent? It just executes.
This is what nobody talks about clearly enough. When you delegate authority to an AI agent, you're not creating a new actor in your system—I'm not even sure that's possible—you're creating a decision surface that has no accountability layer underneath it. And that's the actual problem.
The Authority Gap Isn't About Control
Everyone's talking about governance. Lock it down. Add guardrails. Implement approval workflows. Yes, do those things. But—and I say this as someone who's implemented exactly this kind of system at three different companies—that misses the real issue.
- Governance assumes you know what decisions matter
- Observability assumptions are usually wrong because they're built after the fact
- You can see that an agent made a decision. You can log it. You can trace the inputs. But can you trace why it chose option A over option B when both seemed reasonable? Honestly, sometimes no.
The gap isn't between what the agent can do and what we let it do. It's between the authority we've handed it and the responsibility it can actually bear. An AI agent has no reputation to protect, no salary to lose, no career to build. It just doesn't care what happens when it's wrong. That's not a trust issue. That's a category error.
Continuous Observability Changes the Math
Here's where it gets interesting, though I'm genuinely uncertain whether this solves the problem or just makes it more bearable.
If you're building agent-driven workflows—and if you're not yet, you will be soon—the only thing that actually matters is not whether the agent is trustworthy. It's whether you can see what it's doing in real time and intervene before it propagates damage downstream. Figuring out the why later doesn't help. Your customer already has the wrong order.
Datadog started pushing observability-first thinking for distributed systems back in 2013. The principle was simple: you can't secure what you can't see. Same thing applies here, except it's more urgent. With microservices you have time to investigate an issue. With agents making financial commitments or scheduling critical operations, you have minutes. Maybe seconds.
Continuous observability means instrumenting not just the decision (what did the agent choose) but the decision context (what options existed, what was the confidence score, what constraints were active). Then you build alerting that fires when confidence drops below threshold or when the agent seems to be pattern-matching against scenarios it shouldn't.
Does this solve the authority gap? I don't think so, actually.
The Thing Nobody Wants to Admit
You can observe a bad decision in real time and still not be able to stop it. Network latency exists. Human reaction time exists. By the time you see the alert, the agent has already sent the API call.
And then there's the harder part: you need to decide what counts as a "bad" decision. That requires business logic that lives outside the agent. So now you're building a governance layer. Which means the agent was never really delegated in the first place—it was always supervised. Which means we built an extra step and called it progress.
I'm not sure this is solvable in the way people want it to be. We want autonomous agents because they promise to handle complexity at scale. But scale without accountability just means faster failure. So we add observability. Which means constant human attention. Which defeats the purpose.
Maybe the answer isn't better agents. Maybe it's better delegation frameworks. Things that are actually built for the fact that AI systems can see options but can't own decisions. Things that work with that constraint instead of pretending it doesn't exist.
I'm building toward something like that right now. Early stages. Not sure it'll work.
