AI Delegation > AI Agents

Autonomous AI agents sound great in theory. In practice, there are too many ways for things to go sideways. When AI has direct access to your database, your APIs, and your business systems, you're dealing with something that has side effects everywhere. One bad decision cascades into ten more.

The smarter play is delegation with checkpoints. You hand off specific tasks to AI workflows—multi-step processes where the AI does the heavy lifting, but humans stay in the loop at critical points. The AI processes data, makes recommendations, flags issues. Then it waits. A human reviews, approves, redirects, or stops the process entirely.

Think of it like working with a really fast junior colleague. You give them clear instructions. They do the work. You check the output before it ships. If something looks off, you catch it. Over time, you learn what they're good at and what needs closer review. The AI learns from your corrections. The system gets better.

This hybrid approach plays to the strengths of both sides. AI handles repetitive, time-consuming work that doesn't require judgment calls. Pattern recognition, data extraction, initial analysis—these are perfect AI tasks. Humans handle edge cases, strategic decisions, and anything with serious consequences.

For this to work, you need to design the handoff points carefully. Where does AI hand control back to humans? When does a human need to approve before the next step? Which operations are reversible and which need extra scrutiny? These decisions shape the entire system.

Good handoff design means spotting natural break points in your workflow. After data is extracted but before it's written to the database. After the AI flags potential issues but before any action is taken. After code is generated but before it's merged. These pauses let humans verify without slowing things down too much.

The engineering work happens upfront. You map the process, identify decision points, build in safety stops, and create clear paths for escalation. You're not just building an AI system—you're designing a collaboration framework.

Too many checkpoints and you kill efficiency. Too few and you lose control. Finding the right balance takes iteration. You start conservative, see where humans just rubber-stamp AI decisions, and streamline those. You also watch where things go wrong and add checks.

Edge cases are where this model proves its value. When the AI encounters something weird, it stops and asks. When a decision could have expensive consequences, it flags for review. When the confidence score drops below a threshold, it escalates. Humans handle the tricky stuff without getting buried in routine work.

The feedback loop matters. Every time a human corrects AI output, that teaches the system. Every approval builds confidence in the AI's judgment. Over time, you learn which tasks can run with less oversight and which always need human eyes.

This isn't about replacing automation with AI. It's about upgrading from rigid rules to adaptive systems that still maintain guardrails. The deterministic parts (rules, safety checks, validation) stay deterministic. The probabilistic parts (pattern matching, decision support, content generation) use AI. Together, they create something more robust than either alone.

Fully autonomous agents will find their place in low-stakes environments where mistakes are cheap. For everything else—especially in enterprise contexts—delegation with human oversight is the path forward. The goal isn't full automation. It's high-leverage collaboration between humans and AI.