Enterprise AI Analysis
Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems
Recent advances in agentic AI, combining LLMs with tools, memory, and other agents, enable complex workflow automation. However, evaluating these systems poses significant challenges due to their non-deterministic nature. Our framework addresses this by assessing agent behavior beyond mere task completion, focusing on reasoning, memory, tool use, and environmental interaction to capture critical runtime uncertainties.
Executive Impact
Our framework pinpoints hidden inefficiencies and compliance risks in agentic AI systems that traditional methods overlook, providing a clearer path to reliable AI deployment.
0
Hidden Failures Uncovered
0
Policy Adherence Gaps Revealed
0
Memory Retrieval F1 (S3)
0
Cost Per LLM-as-Judge Evaluation
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
LLM Analysis
Memory Evaluation
Tool Orchestration
Environment Interaction
Ensuring AI Aligns with Enterprise Directives
Our framework rigorously evaluates the Large Language Model's adherence to instructions, safety policies, and alignment with business objectives. Traditional evaluations often miss subtle deviations that lead to compliance risks or operational inefficiencies. We provide granular insights into decision-making processes, identifying where agents might bypass critical steps or make uninformed choices.
67%
LLM Policy Adherence Gap in S1
In Scenario 1 (Cost Optimization), while baseline metrics reported 100% task completion, our framework revealed only 33% policy adherence, indicating actions were taken without consulting safety guidelines.
Optimizing AI's Contextual Understanding
Effective memory management is crucial for agentic AI. Our framework assesses the agent's ability to store, retrieve, and synthesize information from various sources—including historical conversations and external knowledge bases. We uncover issues like low recall or inconsistent memory updates that can lead to misinformed decisions and repetitive errors.
13.1%
Memory Recall in S2 Remediation
In the Security Incident Response scenario (S2), memory recall was particularly low, indicating the agent struggled to retrieve critical context despite successfully completing the task according to baseline metrics.
Validating AI's Actionable Intelligence
Tools allow AI agents to interact with the environment, but their effective use requires precise selection, parameter mapping, and correct sequencing. Our framework evaluates these aspects, identifying misclassifications, incorrect API calls, and missed diagnostic steps that can compromise task reliability and lead to costly operational failures.
Agentic Assessment Process Flow
Test Case Generation
→
Static Validation (Ground Truth)
→
Dynamic Execution (Runtime Monitoring)
→
Judge-based Evaluation (Qualitative)
→
Behavioral Reliability Assessment
7.67
Avg. Tool Failures in S3 (Multi-Agent)
The Tools pillar exhibited the highest failure rate in Scenario 3 (Multi-Agent Root Cause Analysis), primarily due to missed diagnostic or verification steps before remediation actions, underscoring the complexity of tool orchestration.
Ensuring Safe and Compliant Operations
The environment pillar assesses how agents interact with real or simulated systems, including adherence to guardrails, workflows, and resource constraints. We capture failures related to unauthorized actions, policy violations, and improper handling of execution errors, which are critical for maintaining operational safety and compliance.
| Aspect | Conventional Methods | Our Framework |
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| Focus | Task Completion, Output Metrics |
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| Failure Detection |
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| Evaluation Modes |
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| Uncertainty Handling |
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| Feedback Loop |
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Case Study: Multi-Agent CloudOps RCA (S3)
Scenario: Diagnose 60% response time degradation in payment service caused by network misconfiguration. Requires coordination between Performance, Security, and RCA agents.
Problem: Baseline metrics reported task completion (0.62 tool usage ratio), but the framework revealed significant behavioral issues.
Solution: Our assessment framework identified an average of 7.67 tool failures, specifically missed diagnostic or verification steps before remediation, and 3.67 memory failures related to configuration changes, leading to a comprehensive understanding of the root cause.
Impact: Revealed critical failures overlooked by conventional metrics, highlighting the need for systematic assessment of multi-agent coordination, tool orchestration, and memory management in complex scenarios.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing agentic AI solutions with robust assessment.
Estimated Annual Savings
Annual Hours Reclaimed
Your AI Implementation Roadmap
A phased approach to integrate and continuously improve agentic AI within your enterprise, ensuring robust and reliable operations.
Phase 01: Initial Assessment & Pilot
Conduct a deep analysis of current workflows, identify key agentic AI use cases, and implement a pilot program with our assessment framework to validate core functionalities and identify initial behavioral deviations.
Phase 02: Framework Integration & Expansion
Integrate the assessment framework across chosen agentic AI systems. Expand pilot to broader functional areas, focusing on memory, tool, and environment interactions, and refine agent behaviors based on framework insights.
Phase 03: Continuous Monitoring & Optimization
Establish continuous monitoring using static, dynamic, and judge-based evaluation. Implement adaptive strategies for prompt refinement, error recovery, and policy adherence to ensure ongoing reliability and performance optimization.
Phase 04: Scalable Deployment & Governance
Scale agentic AI solutions across the enterprise with robust governance. Leverage framework data for compliance audits, risk management, and strategic decision-making to maximize long-term AI value.
Ready to Assess Your Agentic AI?
Our framework provides the comprehensive insights needed to ensure your AI systems are not just performing, but performing reliably, safely, and efficiently. Book a session to discuss how we can tailor this assessment to your enterprise needs.
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