A Power Outage in Spain: A Glimpse Into Systemic Fragility
On Monday, 28 April 2025, at 12:33 CET, I was standing in the post office in the Madrid suburb where I live when the power went out. The lights cut off. Screens froze. Everything stopped.
Without drama the clerk pulled out an official smartphone, scanned the barcode on my slip, and handed over my parcel. The app she used had cached enough information to complete the transaction—even completely disconnected from the network.
Later, I learned that a voltage dip in the European transmission network had triggered cascading failures across Spain and Portugal. Trains stopped. Airport check-ins stalled. Businesses went dark. In a matter of seconds, one of Europe’s most sophisticated systems showed how complex infrastructure can fail in unpredictable ways.
What mattered wasn’t the failure. It was the response. The system—thanks to redundancies, protocols, and real-world awareness—recovered quickly even with the blackstart . This is exactly the mindset we must bring to testing AI agents: failure may be inevitable, but uncontrolled failure is not.
Why We Must Ask This Question Now
AI agents—especially those built on large language models—are no longer confined to side experiments. They are being introduced into core operational processes: customer support, sales, triage, content generation, scheduling, and more.
These systems are not deterministic. They are stochastic by design. They make decisions probabilistically, retrieve from unstructured memory, and plan based on language inputs. The same prompt can produce different outputs on different days.
In an enterprise environment, trust in systems isn’t optional. It underpins operational continuity, regulatory compliance, customer satisfaction, and brand integrity. Without structured testing, agentic AI becomes a black box—and eventually, a liability.
Why Traditional Testing Falls Short
Deterministic software is straightforward to test. You define the input, know the expected output, and check for accuracy or failure. With enough test coverage, confidence in correctness follows.
Agentic AI doesn’t work that way, rather they:
- Vary their outputs across time and context
- Maintain memory, which alters behavior across interactions
- Use tools and APIs that may fail mid-process
- Plan actions based on goals, not fixed rules
A test suite focused solely on input/output correctness will miss behavioral failures, decision misalignments, and critical safety violations.
What Makes an Agentic System?
An agentic system combines several components to perform autonomous, goal-driven work:
- Reasoning: Using a foundation model (LLM) to interpret context, apply logic, and generate options.
- Memory: Accessing structured and unstructured data across short- and long-term stores.
- Execution: Calling APIs, triggering workflows, or using external tools to act on the environment.
- Planning: Decomposing a task into steps, reflecting on progress, and adjusting based on feedback.
Each component can succeed, degrade, or fail. Testing must evaluate the system as a whole, not just its parts.
Agent Types: Individual, Collective, and Negotiating
Testing an AI agent varies depending on the architecture:
- Single agents can fail by hallucinating, skipping steps, or escalating too early.
- Multi-agent systems introduce coordination risk—two agents may duplicate work, contradict each other, or fail to transfer context correctly.
- Negotiating agents interact with other AI systems. These situations test trust, strategy, and goal alignment—especially when both sides are autonomous.
Testing must include edge cases, role reversals, and real-time failures. The goal isn’t perfection—it’s bounded, observable behavior.
What Can Be Tested?
We can and should test agentic systems for:
- Interpretation of human input: Was intent understood correctly?
- Behavior under uncertainty: Did the agent escalate, pause, or recover?
- Tool orchestration: Were APIs used appropriately and reliably?
- Graceful failure: Did the agent stop safely or push ahead recklessly?
- Adversarial prompts: Can the agent be manipulated into disclosing instructions or policy?
- Inter-agent communication: Are messages sequenced correctly and context preserved?
Crucially, not all failures are explicit. Sometimes, the answer looks plausible—but is subtly incorrect. Sometimes, the agent followed a plan—but the plan itself was flawed.
Frameworks and Methodologies to Apply
Agentic systems require structured, multidimensional testing. Below are three leading approaches, each suited to different stages of development and types of complexity.
Raga.ai’s Holistic 8-Step Evaluation Framework
Philosophy:
Evaluates the full decision lifecycle, not just outputs. It emphasizes planning quality, memory use, tool effectiveness, and safe execution.
Tests for:
- Multi-step reasoning and coherence
- Tool utilization and fallback handling
- Memory precision and state continuity
- Safety violations and escalation readiness
How it works:
- Simulates user prompts (including adversarial and ambiguous cases)
Captures full decision traces with logs at every step - Uses custom metrics like Tool Utilization Efficacy (TUE) and Memory Coherence Rate (MCR)
- Integrated into CI pipelines for continuous revalidation as systems evolve
Strategic value:
This approach is well suited for production-grade agents embedded in business-critical flows. It balances structured automation with traceable decision analytics.
Ref: A Holistic 8-Step Framework for Evaluating Agentic AI Systems
Raga.ai Research, 2024
Prometheus Methodology (Agent-Oriented Software Engineering)
Philosophy:
Provides formal structure to agent design—where each agent has clear goals, plans, and roles within a larger system.
Tests for:
- Goal-plan alignment and sequence integrity
- Correct inter-agent messaging and coordination
- Accurate reactions to environment and stimuli
How it works:
- Starts with system specification: actors, goals, scenarios
- Uses diagrams and interaction protocols to define expected behavior
- Tests are built around verifying correct decision paths and message flows
- Tooling like Prometheus Design Tool (PDT) supports traceable design and evaluation
Strategic value:
Ideal for multi-agent systems with interlocking responsibilities—such as ticket routing, logistics workflows, or approval chains. Ensures each component behaves intentionally within a defined system.
Ref: Model-Based Testing of Multi-Agent Systems
PMC, 2015
Melting Pot (by DeepMind)
Philosophy:
Tests adaptability in social and competitive environments. Designed to surface emergent behavior in agent interactions.
Tests for:
- Cooperation, betrayal, and trust dynamics
- Generalization to unfamiliar agents or conditions
- Stability across varying incentive models
How it works:
- Provides diverse simulation environments (social dilemmas, negotiation games, collaboration challenges)
- Measures emergent behaviors and generalization capability
- Evaluates strategic robustness rather than just correctness
Strategic value:
Useful when deploying agents into open or competitive environments, or when agents must adapt to third-party behaviors (e.g., marketplaces, procurement, supply chain negotiation).
Using SOPs as a Testing Backbone
Most enterprises already rely on Standard Operating Procedures—formal or informal. These documents or logs outline how a process is meant to run. For now, SOPs can serve as an anchor for testing agentic systems.
We can evaluate whether an agent:
- Follows SOPs exactly
- Deviates appropriately to optimize or adapt
- Produces outcomes that violate, extend, or reframe known procedures
Emergent behavior is not inherently bad. But it must be observable, interpretable, and containable. In the future, agents may generate their own SOPs. For now, ours provide the rails to keep autonomy aligned.
What Aviation Can Teach Us About Testing AI
Aviation is built for resilience under uncertainty. Systems are designed to handle unpredictability—weather, mechanical faults, human error—without catastrophic failure.
Pilots train in simulators. Aircraft include redundant controls. Black boxes record every step of every flight. Autopilot systems are bounded in scope and always subject to human override.
This is a mature model for managing stochastic autonomy. Agentic systems should follow suit.
- Simulate edge cases
- Capture detailed traces
- Define handoff points to humans
- Audit behavior continuously
Like aviation, we don’t expect agents to never err. We expect them to err safely—and visibly.
What Enterprises Should Demand from Agentic Systems
Modern enterprises need more than feature lists and benchmarks. They need confidence.
You should expect:
- Predictability: Agent behavior is bounded and reproducible within defined ranges.
- Traceability: Logs, decisions, and reasoning paths are reviewable.
- Escalation: Agents defer to humans when confidence drops or policy risks emerge.
- Observability: Metrics, alerts, and dashboards monitor system health.
- Resilience: Systems fail gracefully and recover predictably.
These are the pillars of AI assurance—not just performance.
Conclusion: Building Trust in Intelligence, Not Just Performance
Agentic AI is powerful, flexible, and ready for real-world deployment. But trust does not emerge from output quality alone. It comes from reliability, recoverability, and respect for operational guardrails.
We are not testing math models. We are testing decision-makers. Software that plans, adapts, and executes in the real world must be evaluated like pilots, not spreadsheets.
The new frontier isn’t AI safety or AI productivity. It’s AI assurance engineering—the discipline of building intelligent systems we can trust.