We build agentic AI systems the way Victor Frankenstein built his creature: stitching together parts that each work well in isolation — a language model here, a tool-calling framework there, a vector database, a scheduler, a handful of subagents — and animating the whole thing with just enough orchestration logic to make it move. Then we’re surprised when it develops a mind of its own.
How the monster gets assembled
Most “agentic AI Frankensteins” aren’t the result of one bad decision. They’re the accumulation of many reasonable ones:
- Bolted-on autonomy. A tool-using chatbot quietly gains the ability to loop, retry, and call other agents — without anyone revisiting the original safety assumptions.
- Mismatched parts. Different subagents are built on different models, prompting conventions, and error-handling philosophies, so failures propagate in ways no single team fully understands.
- No one owns the whole. Each component has an owner. The emergent behavior of the system as a whole belongs to no one until it breaks in production.
- Guardrails added after the fact. Permissions, rate limits, and human-in-the-loop checks get retrofitted once something goes wrong, rather than designed in from the start.
Why it matters
A single model that hallucinates is a nuisance. A chain of agents that hand off tasks, retry on failure, and take real-world actions (sending messages, spending money, modifying infrastructure) can turn a small mistake into a cascading one — especially when the failure mode wasn’t anticipated by any single component’s designer.
Stitching it together more carefully
- Design the seams, not just the parts. Treat handoffs between agents as a first-class interface with explicit contracts, not an afterthought.
- Bound autonomy deliberately. Decide up front which actions require human confirmation, and keep that list visible as capabilities grow.
- Make the system observable as a whole. Logging each agent’s output isn’t enough — you need to see the full chain of decisions that led to an outcome.
- Assign ownership of emergent behavior. Someone should be accountable for what the assembled system does, not just what each piece does.
- Test the monster, not just the muscles. Unit-test each agent, but also run end-to-end scenarios that exercise the full multi-agent loop under failure conditions.
The lesson from Shelley’s novel was never “don’t build anything ambitious.” It was that ambition without responsibility for the whole creation is where things go wrong. The same is true for agentic AI: the risk isn’t in connecting powerful components together — it’s in doing so without anyone accountable for the creature that results.
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