How to Evaluate Agentic AI Solutions Before Committing to a Software License

As enterprise software continues to evolve, agentic AI solutions—those capable of autonomous decision-making and action—are transforming how businesses automate complex processes. Unlike traditional automation that follows rigid rules, agentic AI promises true autonomy, continuous learning, and intelligent adaptation to new scenarios. But with significant investments at stake and a market filled with both established players and innovative startups making bold claims, how do you ensure you're selecting a genuinely autonomous solution before signing on the dotted line?

This guide provides a structured approach to evaluating agentic AI solutions, with a particular focus on vertical process automation technologies designed for specific domains like finance, procurement, and operations. For a more comprehensive exploration of this topic, stay tuned for our complete white paper that we will publish by the end of this month: “In Pursuit of Autonomy: A No-Nonsense Guide to Choosing Your Agentic Process Automation Partner.”

Watch this short Video to understand the new workflow of human-machine collaboration with agentic AI:
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1. Understand the Distinction: True Agency vs. Basic Automation

Many solutions claim to be "AI-powered" but offer little more than rule-based automation with a marketing veneer. True agentic AI demonstrates:
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• Autonomous decision-making beyond predefined rules and conditions

• Contextual understanding of business processes and document semantics

• Dynamic learning that improves performance without explicit programming

• Adaptive reasoning to handle novel situations and exceptions

• Goal-oriented behavior that optimizes for business outcomes, not just task completion

When evaluating solutions, probe beneath surface-level claims to verify these fundamental capabilities of genuine agency.
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2. Evaluate Practical Autonomy in Your Specific Business Context

Agentic capabilities must translate to meaningful autonomy in your specific business processes:
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• Request demonstrations using your actual documents and complex decision scenarios

• Test how the system reasons through ambiguous information requiring judgment

• Evaluate the agent's ability to maintain context across multiple steps in a process

• Assess how the system's Chain-of-Thought reasoning handles exceptions specific to your business

To guide these demonstrations effectively, use our "Top 10 Questions to Ask in an APIA Vendor Demo" resource, which provides pointed questions designed to distinguish truly agentic solutions from cleverly marketed traditional automation.

An agentic solution should demonstrate transparent reasoning processes, not just provide black-box outcomes. Look for systems that can explain their decisions and show how they learn from corrections.

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3. Assess the Agent Architecture and Intelligence Model

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The underlying architecture of an agentic system significantly impacts its capabilities:
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• Multi-agent frameworks that coordinate specialized agents for different tasks often outperform single-agent approaches

• Large Language Models (LLM) integration strategies determine how effectively the system leverages foundational models

• Retrieval Augmented Generation (RAG) capabilities enable the system to ground decisions in your business context and policies

• Agent orchestration mechanisms determine how effectively multiple AI components work together

Probe vendors on their specific approaches to these architectural elements, as they directly impact autonomy levels and performance in complex scenarios.

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4. Insist on a Structured Proof of Value with Your Data

Never commit to an agentic AI solution without a structured Proof of Value (POV) that validates autonomous capabilities:
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• Use a statistically significant sample representing your full document diversity

• Include scenarios that require complex decision-making, not just data extraction

• Test exception handling and the system's ability to learn from corrections

• Evaluate performance metrics specific to agency: autonomous completion rates, decision quality, learning efficiency

• Establish a defined timeline (typically 4-8 weeks) with clear evaluation milestones

The POV should demonstrate not just technical capabilities but practical business outcomes in your environment. For a complete evaluation framework, download our "APIA Vendor Evaluation Scorecard" which provides a comprehensive template for assessing vendors across all critical dimensions of agentic capabilities.

5. Evaluate Continuous Learning Capabilities

True agentic systems demonstrate sophisticated learning behaviors:
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• Few-shot learning abilities that allow the system to adapt to new document types with minimal examples

• Feedback integration mechanisms that improve performance based on user corrections

• Transfer learning capabilities that apply knowledge from one domain to similar scenarios

• Concept drift detection that identifies when process or document patterns are changing

Request concrete evidence of how the system improves over time with metrics showing accuracy improvements correlated with usage duration.

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6. Scrutinize the Data Security and AI Governance Framework
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Agentic AI solutions require more sophisticated security and governance considerations:
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• Understand how the system protects business data used for training and inference

• Verify that model tuning doesn't compromise data confidentiality

• Evaluate audit trails for autonomous decisions to ensure transparency and compliance

• Assess controls for preventing unintended agent behaviors or decision biases

• Verify compliance certifications relevant to your industry's AI governance requirements

This is especially important for solutions leveraging large language models, which may have different security considerations than traditional software.

Also refer to our blog post about Common Security Concerns About AI Document Processing- And How Hypatos Resolves Them.

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7. Uncover the True Total Cost of Ownership for Agentic Systems

Agentic AI solutions often have different cost structures than traditional automation:
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• Implementation costs including agent configuration and training

• Ongoing model fine-tuning and optimization expenses

• Monitoring and governance overhead for autonomous systems

• Integration complexity with existing systems and workflows

• Scaling costs as usage grows and agent capabilities expand

Request detailed TCO scenarios that account for the unique aspects of maintaining an agentic system compared to traditional automation.

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8. Verify Agentic Claims with Reference Customers‍

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Reference checks are critical for validating claims about autonomous capabilities:
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• Request references from organizations using the solution for similar complex processes

• Ask about autonomous processing rates for different document and decision types

• Inquire about the learning curve and time to achieve significant autonomy

• Discuss how the system handles edge cases and ambiguous scenarios

• Evaluate the balance between autonomy and appropriate human oversight

The most revealing insights often come from asking: "In what scenarios does the system still require significant human intervention, and has this changed over time?"

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9. Evaluate the Vendor's AI Research and Innovation Trajectory‍

The agentic AI landscape is evolving rapidly, making the vendor's innovation capacity critical:
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• Assess their research team's expertise in key areas: multi-agent systems, LLMs, RAG, etc.

• Review their published research or technical blog posts on agent technologies

• Evaluate their approach to incorporating emerging AI capabilities into their product

• Assess how frequently they release meaningful improvements to agent capabilities

• Consider their partnerships with academic institutions or foundational model providers

Your investment should align with a partner whose innovation trajectory matches your long-term automation ambitions.

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10. Negotiate Contract Terms That Protect Your Agentic AI Investment

Once you've decided to move forward, contract terms become crucial:
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• Include performance guarantees tied to specific autonomy metrics

• Negotiate data usage rights, particularly for model training and improvement

• Secure access to performance analytics and decision audit trails

• Include specific SLAs for agentic performance, not just system uptime

• Build in exit clauses if autonomous capabilities fail to meet agreed benchmarks

Consider including a production pilot phase with an opt-out clause before committing to a full-term contract.

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Conclusion: A Strategic Approach to Agentic AI Investment

Agentic AI solutions represent the next frontier in business process automation, offering unprecedented levels of autonomy and intelligence. However, they require a fundamentally different evaluation approach than traditional software. By focusing on practical autonomy, agent architecture, learning capabilities, and appropriate governance, you can distinguish truly transformative solutions from conventional automation wearing an AI disguise.

Remember that impressive demos of predetermined scenarios tell you little about true agency. The real test is how the system performs against the full complexity and variability of your business processes, and how it learns and improves over time.

Contact us today to receive these resources and start your journey toward truly intelligent automation with confidence.