Closing the Gap Between First and Second-Order Thinking in agentic workflows
Why operators struggle to instruct agents and what it takes to make workflows dependable when domain experts are not trained in AI or software logic.
I was recently speaking with a Principal at KKR, and one part of that conversation has stayed with me.
We were discussing why agent workflows look promising in demos, then break down in real business operations. He articulated the problem very clearly through first-order and second-order thinking.
First-order is what people usually say: “move invoices from one folder to another and process them.”
Second-order is the layer they rarely say out loud: do not process duplicates, validate critical values, flag invalid status patterns, and handle messy exceptions before they become risk.
Most operators in front and back office teams are experts in finance, economics, and operations. They are not trained in computer science, prompt design, or AI failure modes. So asking them to define production-grade agent logic from scratch is not realistic.
When teams only provide first-order instructions and expect second-order outcomes, the workflow becomes unreliable. That is usually when the label becomes “AI hallucination,” when the real issue is missing operational logic.
This is the kind of design principle that can change how teams solve problems with agents in industry. At Titan, we believe the gap between first-order and second-order thinking should be closed through the product experience.
Titan is a data reasoning and provenance layer for regulated workflows, designed to make operational decisions explicit, traceable, and dependable.
The product question for us is not, “How do we improve the prompt box?” It is, “How do we help operators surface the logic they already hold in their heads through experience?”
Our current approach is to make workflow definition a structured dialogue, not a single instruction.
- What cannot fail?
- What counts as invalid?
- What should happen when data conflicts?
- What needs human review before completion?
If we get this right, agent workflows become more authoritative and more useful for everyday teams. Not because everyone became an AI expert, but because the system helped make second-order logic explicit before automation runs.
For Titan, this is a core design direction. Build with the operator’s reality, then formalize the hidden guardrails that make the work safe.