5 Trust Barriers Blocking Agentic AI Adoption and How to Overcome Them

AI agents sound great in theory. Systems that can think through problems, make decisions, and take action without constant human oversight could save companies enormous amounts of time and effort. But most organizations aren't rushing to deploy them, despite all the hype.

The hesitation makes sense when you dig into it. Companies need to understand what their AI agents are doing and why. When an AI agent makes a decision that costs money or affects customers, someone needs to be able to explain that decision to their boss, their board, or a regulator. 

This creates an interesting paradox. The more autonomous you make an agentic AI system, the less visibility you have into its decision-making process. Companies want the benefits of automation, but they cannot risk giving up control entirely.

Forrester's research on "The State of AI Agents" surveyed organizations to understand the key barriers preventing widespread AI agent adoption. The study identified five critical trust priorities that companies consistently raise when evaluating these systems for deployment.

In this blog, we'll address each of these five trust barriers identified in the Forrester study and show how KNIME addresses each one.

The Problem: AI agents often work through complex reasoning chains that humans can't easily follow. When something goes wrong, you're left trying to reverse-engineer what happened.

Why This Matters: Try explaining to your CFO why the AI agent just cancelled a million-dollar contract because it didn’t pick up a clause in the agreement correctly. If you can't walk through the reasoning step by step, you'll have a hard time preventing it from happening again. Transparency and explainability are also important things you need to ensure to comply with both the EU and US AI acts. 

How KNIME Helps: 

• Visual workflows in KNIME provide transparency into the AI agent's operational framework. You can see what tools the agent has access to, the specifications and constraints for each tool, and the overall structure of how the agent approaches tasks. You maintain visibility into the agent's available capabilities and the guardrails that govern its actions.
• When you need to understand why an agent made a particular choice, you can trace through the workflow to see the context, constraints, and data processing steps that influenced the AI's decision-making environment.
• While you can't peer into the AI model's reasoning process, you can see and control everything that shapes what the AI sees and how it can act, significantly reducing the size of the "black box."

The Problem: AI agents need access to lots of data to work effectively, but equipping actors with extensive privileges is against IT’s security best practices, especially when it comes to autonomous systems.

Why This Matters: Data breaches, privacy violations, and intellectual property leakage represent existential risks for many organizations. They bring regulatory fines, lawsuits, and destroy customer trust. When an AI agent causes a breach because it had too much data access, it looks like the company lost control of its own systems.

How KNIME Helps: 

• You can build workflows that act like guardrails to automatically scan and filter data before it reaches external LLM providers. These workflows detect sensitive information patterns, apply classification rules, and route data to LLMs accordingly. 
• You can create workflows that automatically anonymize personally identifiable information before any data gets processed by GenAI models.
• You maintain complete control over where your data lives and which models can access it. 
• If you want to keep everything on-premises, you can do that.
• You can configure which external LLM providers your AI agent uses for its reasoning, and separately control which internal and external tools the agent can access during execution.

The Problem: AI systems inherit biases from their training data, and AI agents can act on those biases thousands of times per day without human oversight. If your AI agent is pulling information from siloed systems such as your CRM, ERP, ticketing system, and other tools, it might be getting incomplete or skewed views of customers or situations.

Why This Matters: A human making biased decisions might affect dozens of customers per day. An AI agent making biased decisions could affect thousands of customers per hour. The scale amplifies both the business impact and the legal risk. 

When agents work with fragmented data from different systems, they might make decisions based on partial information that reinforces existing biases. For example, an agent might consistently rate customers from certain regions as high-risk simply because it only has access to complaint data but not purchase history or satisfaction scores.

How KNIME Helps: 

• You can build bias detection directly into your workflows and ensure agents get access to complete, representative data.
• Instead of letting your AI agent pull information from isolated systems that might give biased views, KNIME lets you create integrated data tools that provide holistic customer or situation views. 
• Next, you can implement bias detection checks at multiple points in the AI agent pipeline, from data preprocessing through model evaluation with the KNIME Giskard extension. 
• You can establish guardrails within workflows to flag potentially biased decisions before they're executed, and you can ensure your agents are working with the full picture rather than fragmented data that might lead to skewed conclusions.

The Problem: AI agents need enough freedom to be useful, but companies worry about agents taking actions that are technically correct but contextually inappropriate.

Why This Matters: An AI agent might decide to automatically reject all loan applications from a certain zip code because the data shows higher default rates there. Technically, it's reducing risk. Practically, it's creating a PR nightmare and potential legal liability.

How KNIME Helps: 

• You can set up automated validation rules within your workflows that check each AI decision against compliance requirements, business policies, and ethical guidelines before any action is taken.
• You can build in human approval checkpoints for certain types of decisions, ensuring that high-risk or sensitive actions always get human oversight regardless of the AI's confidence level.
• You can implement escalation logic that automatically routes edge cases or potentially problematic decisions to appropriate reviewers based on your organization's approval processes and risk tolerance.
• Administrators can monitor these real-time guardrails through intuitive data apps, seeing what decisions are being flagged, approved, or escalated as they happen.

This approach means the controls aren't bolted on as an afterthought, they're part of how the AI agent operates from day one.

The Problem: AI agents work fast and process lots of decisions. Traditional monitoring approaches can't keep up, which means problems often go undetected until they've caused significant damage.

Why This Matters: By the time you notice that your AI agent has been making poor decisions, it might have already processed thousands of cases. The cost of fixing those mistakes can be enormous, both financially and reputationally.

How KNIME Helps: 

• You can configure traceability to retrieve data flows between tools, giving you the flexibility to set up the level of audit trail that matches your specific monitoring and compliance requirements.
• You can get automated workflow summaries for audit and compliance. Nodes and templates are available for easy summarization of workflow logic that can be automatically reported to key stakeholders.
• You can track agent performance over time by calculating key metrics within the workflow such as success rates, latency, confidence scores, or error types and visualizing them. This helps identify when an agent is underperforming or behaving unexpectedly.
• Since agents rely on models to make decisions, KNIME makes it easy to switch to newer or better-performing ones as needed. It's tech stack agnostic, so it fits into your setup today and stays flexible for whatever comes next. 

The companies succeeding with AI agents are the ones that solved the trust problem first.

They started by acknowledging that AI agents need to earn trust through demonstrable reliability. They built systems that provide visibility into decision-making and maintain human oversight where it matters.

KNIME's approach works because it treats these concerns as engineering problems rather than abstract risks. Visual workflows provide transparency by design. Flexible deployment options address security concerns. Built-in monitoring and control mechanisms handle governance requirements.

The organizations deploying agents successfully are those that take implementation seriously. They're building trust systematically rather than hoping it will emerge naturally.