Why Agent Workflows Fail Before They Even Begin

Agent workflows don’t usually fail because the model “hallucinates.”
They fail because the DAG is wrong before the first model call even happens.

After reviewing real-world agent systems, the same failure patterns appear again and again — and none of them are caused by the LLM.

This post breaks down the structural issues that make agent workflows brittle, and how to fix them.

1. The Real Problem: The DAG Lies

A Directed Acyclic Graph (DAG) should define:

• task order
• dependencies
• inputs + outputs
• verification flows
• failure paths

But most DAGs used in agent systems don’t reflect reality.

Common mismatches:

• a node expects data that upstream nodes never provide
• task names imply behavior that isn’t defined
• tool schemas are incomplete
• execution order doesn’t match actual data needs
• missing validation allows silent corruption

When the DAG is wrong, the workflow becomes unpredictable — even if the model is perfect.

1. The Five Structural Failure Modes

These are the root causes you’ll see in almost every broken agent workflow.

1. Vague Task Definitions

Example of a weak node:

Node: Analyze requirements
Input: Document
Output: Summary

This looks fine until another node tries to consume the “summary.”

But:

• what structure should it have?
• what counts as required information?
• what’s the expected format?

Ambiguity cascades downstream.

Symptoms:

• inconsistent output
• models improvising structure
• downstream parse failures

2. Missing Verification Nodes

Most workflows skip validation completely:

• no schema check
• no assumption check
• no consistency check
• no correctness check

Without verification:

• errors propagate silently
• models correct the wrong things
• downstream tasks become unstable

Fix:
Insert explicit validate → correct steps between major nodes.

3. No Retry Logic for External Tools

LLMs are predictable compared to:

• flaky APIs
• network timeouts
• unpredictable parsers
• variable runtime tools

If the DAG doesn’t retry:

• a single failure collapses the workflow
• errors appear random
• systems appear “fragile”

Always add:

• 2–3 retries
• exponential backoff
• fallback paths if the tool fails completely
• 4. Circular or Implicit Dependencies

A hidden loop looks like this:

A → B
B → C
C → A

Each link seems logical, but the full cycle leads to deadlock.

Models can't resolve missing context, so they guess which looks like hallucination even though it's a graph error.

5. Poor Tool Definitions

Tools are often defined like this:

Input: string
Output: JSON

But in reality, tools return:

• arrays sometimes
• objects sometimes
• partial fields
• undocumented error states
• inconsistent keys

If the agent doesn’t know the real structure, it improvises.

1. Text-Based Diagram: Agent DAG Failure Map

┌─────────────────────────────┐
│      DAG FAILURE SOURCES    │
└─────────────────────────────┘
             │
             ▼
┌─────────────────────────────┐
│  Vague Task Definitions     │
└─────────────────────────────┘
             │
             ▼
┌─────────────────────────────┐
│  Missing Verification Node  │
└─────────────────────────────┘
             │
             ▼
┌─────────────────────────────┐
│  No Retry Logic             │
└─────────────────────────────┘
             │
             ▼
┌─────────────────────────────┐
│  Circular Dependencies      │
└─────────────────────────────┘
             │
             ▼
┌─────────────────────────────┐
│  Poor Tool Definitions      │
└─────────────────────────────┘

This failure sequence happens before any meaningful model inference.

1. Edge Cases Developers Often Miss • Partial Output Drift

Only 60% of the expected fields appear → downstream nodes fail quietly.

• Cross-Task Assumption Leaks

Node B assumes Node A completed perfectly.

• Non-Deterministic Inputs

Timestamp changes or ordering changes affect the entire workflow.

• Tool Race Conditions

Two tool outputs arrive in different orders each run.

• Context Reuse Errors

Agents re-read old context and amplify outdated assumptions.

1. How to Fix Agent Workflow Reliability Before Calling the Model ✔ Define strict input/output schemas for every node

No ambiguity → no drift.

✔ Add validation between transformations

Don’t trust any output blindly.

✔ Add retries for all external tools

Design assuming tools will fail.

✔ Run cycle detection on the DAG

Never let hidden loops execute.

✔ Document real-world tool behavior

Include failure modes and edge cases.

✔ Validate the DAG before execution

Static analysis prevents runtime chaos.

1. Takeaway

Agent systems don’t fail because LLMs are unreliable.
They fail because the workflow structure is wrong.

If the DAG is ambiguous, incomplete, or contradictory, the model can’t compensate for the missing structure.

Fix the DAG first.
The intelligence comes after.

Comments

[rokoss21]

This nails the real failure mode: agents don’t fail at reasoning, they fail at execution contracts.

Most “hallucinations” are just the model being forced to guess because the DAG underspecifies reality — missing schemas, implicit assumptions, undefined failure paths. At that point the system is already non-deterministic before inference starts.

We ran into the same conclusion while working on FACET: once you treat the workflow itself as a first-class artifact that must be validated, versioned, and testable, model behavior becomes dramatically more stable.

Garbage DAG in, garbage intelligence out.