Most AI agent demos work beautifully.
Then you ship them into a real system.
And suddenly:
- agents hallucinate confidently
- steps get skipped
- tools are called out of order
- outputs drift from the original intent
Teams usually respond by tweaking prompts.
That almost never works.
This post explains why AI agents fail in production, what’s actually going wrong under the hood, and why the problem isn’t models — it’s missing planning.
The Core Misunderstanding About AI Agents
Most agent systems today rely on this assumption:
If we give the model enough context and a good prompt, it will behave correctly.
That assumption breaks the moment agents:
- run across multiple steps
- call real tools
- modify state
- interact with external systems
At that point, implicit intent is not enough.
Failure Mode 1: Agents Predict, They Don’t Decide
Large language models don’t reason about truth or correctness.
They:
- predict the most likely next token
- optimize for plausibility, not accuracy
- have no built-in concept of “this decision was already made”
So when an agent is asked to act repeatedly:
- earlier decisions are re-interpreted
- assumptions silently change
- the plan shifts without warning
This is where hallucinations start.
Not because the model is bad — but because nothing is anchoring decisions.
Failure Mode 2: Implicit Planning Collapses Under Execution
In many systems:
- the “plan” exists only inside a prompt
- constraints are implied, not enforced
- nothing separates intent from execution
This works in short conversations.
It fails when:
- tools are involved
- workflows span minutes or hours
- multiple agents collaborate
- retries or partial failures occur
When execution starts, the agent is forced to re-derive intent from scattered context.
That’s not planning.
That’s improvisation.
Failure Mode 3: Prompting Harder Makes Things Worse
When systems fail, teams often:
- add more instructions
- add longer prompts
- add more examples
- add more “DO NOT” rules
This increases:
- token usage
- cognitive load
- ambiguity
But it does not create determinism.
You’re still asking the model to infer decisions at runtime — repeatedly.
That’s why prompt-only agents drift.
The Missing Layer: Explicit Planning
Reliable agent systems separate three distinct phases:
Intent definition
What problem are we solving? What constraints apply?Planning
What decisions are locked? What steps are allowed? What assumptions are fixed?Execution
Agents act only within the bounds of the plan.
Most systems skip step 2.
That’s the bug.
Why Planning Must Be Explicit (Not Prompted)
An explicit plan:
- exists outside the model’s hidden state
- survives retries and failures
- can be inspected, validated, and versioned
- prevents agents from silently changing assumptions
Once decisions are written down:
- agents stop hallucinating alternatives
- execution becomes constrained
- failures become debuggable
This is how you reduce drift without over-prompting.
Planning in Practice
Some teams are starting to introduce a thin planning layer before agents execute — a place where intent, constraints, and decisions are made explicit and locked.
That’s the direction tools like Superplan take: treating planning as a first-class artifact instead of something inferred repeatedly at runtime.
If you’re interested in that approach, you can see the idea here:
What This Means for Production AI
If you’re building agents that:
- call APIs
- touch infrastructure
- write data
- trigger workflows
Then you don’t have a “prompting problem”.
You have a planning problem.
Until intent is explicit and decisions are locked before execution, hallucinations are inevitable.
Closing Thought
Models will keep improving.
Tooling will keep evolving.
But no amount of model quality fixes a system that never decided what it was doing in the first place.
Planning isn’t overhead.
It’s the difference between a demo and a production system.
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