User trust erosion: Are we using Agent AI to accelerate a well functioning platform for efficiency, or are we using it to mask platform deficiencies you never wanted to fix in the first place?

When AI is used to compensate for poor workflows and software problems that there was no “funding” to fix in the first place, your organization can become dependent on AI workarounds or shortcuts layered on top of existing problems. So, what is the problem with that?

It is no surprise that agents interacting with “imperfect systems” can produce inconsistent results, which, in the end, will require extensive monitoring and create new failures that are harder to diagnose than traditional software defects. Now you are not even able to create a Jira ticket, are you?

Relying on AI can cause user trust erosion. If agents are making mistakes because they are interacting with incomplete or inconsistent upstream or downstream systems you own, and those systems never get audited, never generate audit trails to start with, or are never tested properly, users may lose confidence in both the AI tools you integrated and the surface platform they interact with. Surprise: you are responsible for both.

Integrating an AI platform into an imperfect system is like placing an intelligent autopilot on top of an aircraft with a broken engine. The autopilot may compensate for many of the underlying issues and make the flight appear smoother, but it can't fix the engine. When something goes wrong, diagnosing the root cause becomes harder because the failure could originate from the autopilot, faulty instruments, or the interaction between the two.

Assuming AI will fix platform deficiencies instead of addressing them directly, while failing to categorize, audit, and properly test agent outcomes can lead to astonishing results. Below you will see some of the failure types that need to be tested, categorized and worked on after integrating AI Agents.

Failure Type	Example	Root Cause
AI reasoning failure	Agent misunderstands intent and chooses wrong action	Model/agent (Agent Failure)
Tool execution failure	Agent calls wrong API or uses incorrect parameters	Agent orchestration (Agent Failure)
Platform deficiency	API returns incomplete or inconsistent data	Platform (Platform Failure)
Data quality issue	Source system contains incorrect records	Data (Platform Failure)
Process gap	Business rule is undocumented or ambiguous	Process (Process Deficiency)
Human interaction issue	User gave unclear instructions so AI wasn’t successful	User education or process deficiency

When integrating an AI platform, we shouldn't assume that efficiency alone is success. That said, AI cannot inherently know that your platform is deficient, that an API returns incomplete information, that a workflow is broken, or that a system contains inconsistent data. It can only operate within the environment it is given. Befittingly, AI will never be 100% accurate, but we can build audit trails so that every decision can be understood, audited, and diagnosed when needed.

Every agent action should generate an audit trail:

• what user requested

• how agent interpreted the request

• which tool or API selected

• what are the input parameters

• how the system responded

• the decision of the agent

• the final outcome

  
  

Without this traceability, when the system fails for any of the reasons described above, teams tend to blame the AI and assume the underlying platform was functioning correctly. In reality, the failure may have originated from a platform deficiency that already existed before the AI was introduced.

This is why the AI integration should never be used to mask platform deficiencies. Those deficiencies should be identified and fixed consistently, not only as part of the integration effort. Otherwise, when failures occur, it becomes difficult to determine what actually happened and why. Was it the AI? Was it the platform? Or was it the interaction between the two?

This is only one aspect of the challenges organizations face when integrating AI into existing platforms. I will explore additional considerations and lessons learned in future posts.

Stay tuned.