Why Agents Fail at Logic (and How to Fix It)
- Mandy Lee
- Dec 15, 2025
- 3 min read
The gap between reasoning and reliability
AI agents can write code, summarize documents, plan tasks, and even run commands.
But ask them to do some simple arithmetic and suddenly your agent regresses from your efficient intern to a junior school class clown.
The problem isn’t that LLMs can’t reason effectively.
They can’t reason consistently. Ask the same question 10 times and you won’t get the same answer 10 times.
For some use-cases, that’s a benefit. For others, like risk calculations, pricing rules, or scoring systems, that’s a liability.
That’s why most “autonomous” agents are still failing—brilliant at demos, unreliable in production.
Where things fall apart
“When presented with an error, my AI Agent hallucinated an API to try and solve the problem,” Hannah Foxwell shared one of the strangest issues her recent agent team encountered during development.
When you ask an agent to automate a multi-step process, three failure patterns show up again and again:
Hidden assumptions – The agent fills in missing details on its own, inventing logic that looks right but isn’t.
Drift across steps – Even small per-step error rates accumulate through a multi-step workflow, turning minor deviations into major failures.
No explainability – You can’t see why it made a decision or where the logic went wrong. Debugging often becomes a matter of guesswork, and prompt engineering alone lacks precision.
That’s fine for simple tasks. But when the outcome matters—billing, compliance, safety, security—“close enough” isn’t close enough.
Why more prompting won’t fix it
Every team building with agents eventually learns the same lesson:
Prompt engineering will only get you so far.
“I needed an Agent to multiply two numbers together and calculate a percentage. Simple right? Not really. I had to wait for my colleagues in engineering to provide me with a tool I could use for this purpose,” Hannah Foxwell shares her frustration that she couldn’t solve a simple problem without pulling engineers away from their development work.
You can upgrade your model, add guardrails, or build eval tests—but you’re still relying on a probabilistic model to execute deterministic logic. This is a solved problem, it's just not solved by a large language model. Choose the right tool for the job.
What agents are actually good at
Let’s give them credit where it’s due.
Agents excel at:
Understanding human intent.
Translating messy inputs into structured instructions.
Coordinating between APIs and tools.
They’re natural orchestrators.
What they lack is a reliable substrate on which to run real logic.
The missing piece: a logic layer they can trust
That’s where Leapter comes in.
Instead of asking agents to invent logic, Leapter lets humans design it.
Teams map out complex logic visually—conditions, loops, validations, calculations and data relationships—until the blueprint behaves exactly as intended.
The agent can then use that blueprint as a verified tool like we humans use a pocket calculator or an excel sheet.
It doesn’t need to reason about edge cases or invent missing logic.
It simply runs what’s already been tested and approved.
Now the relationship flips:
The human defines the system logic.
The agent executes it safely.
The output is explainable, auditable, and always correct.
From prompt engineering to tool engineering
When the logic lives in a visual, executable blueprint, agents stop guessing and start performing.
You can trace every decision. You can validate every branch.
You can finally automate with confidence.
It’s not about building smarter agents.
It’s about giving them smarter tools.
Building real-world reliability
AI Agents are transforming how work gets done. For real people and real businesses.
Real work requires reliable, consistent, precise execution.
That’s the core idea behind Leapter:
Make logic visible, explainable, and something that both humans and agents can share.
Because when AI agents can finally rely on logic, they don’t have to improvise, they stop failing—and start delivering.
Try out one of our demo tools, such as this pricing engine, and see if for yourself!
