This is a submission for the Google Cloud NEXT Writing Challenge
We Can Build AI Agents After Google Cloud NEXT ‘26 — But We Can’t Test or Debug Them
At Google Cloud NEXT ‘26, we were handed something powerful:
Systems that can plan, decide, collaborate, and act.
With A2A enabling agent-to-agent communication, ADK accelerating agent development, and Vertex AI orchestrating intelligent workflows at scale, one thing is clear:
We’ve entered the era of autonomous software.
But beneath that progress lies a problem most developers haven’t fully processed:
We can build these systems faster than we can understand, test, or debug them.
The Hidden Engineering Crisis
Traditional software depends on a simple guarantee:
Same input → same output
That’s what makes testing possible.
- Unit tests validate logic
- Regression tests ensure stability
- Bugs can be traced and fixed
But AI agent systems don’t behave like that.
They are:
- non-deterministic
- context-sensitive
- dynamically adaptive
Which means:
The same input can lead to different reasoning paths, different tool usage, and different outcomes.
And suddenly—
Testing, as we know it, starts to collapse.
What Google Cloud NEXT ‘26 Actually Changed
Google didn’t just launch tools.
It introduced a new class of systems:
- A2A → agents interacting unpredictably
- ADK → workflows that evolve at runtime
- Vertex AI → orchestration across distributed intelligence
These aren’t just applications.
They are behavioral systems.
And behavioral systems don’t fail like code.
They fail like decisions.
The Testing Gap (The Problem No One Named)
We now face a new engineering reality:
The Non-Deterministic Testing Gap
We can:
- build agents
- deploy them
- scale them
But we cannot reliably:
- predict behavior
- test all possible paths
- guarantee consistency
We are shipping systems we cannot fully verify.
Case 1: Autonomous Billing Failure
Consider a multi-agent billing system:
- Agent A → handles customer queries
- Agent B → validates transactions
- Agent C → executes refunds
A user reports:
“I was charged twice.”
The system responds:
- Agent A interprets intent
- Agent B performs partial validation
- Agent C issues a refund
But the charge was valid.
At scale?
This isn’t a bug.
It’s a systemic behavior failure.
Case 2: Healthcare Triage Drift (High-Stakes)
Now imagine a triage assistant:
- prioritizes patients
- suggests urgency levels
- routes decisions
In testing:
- it performs correctly
In production:
- slight variation in phrasing
- subtle context differences
Result?
A critical case is deprioritized—not due to error in code, but variation in interpretation.
This is not deterministic failure.
This is behavioral drift under uncertainty.
Debugging Is No Longer Debugging
In traditional systems:
- you trace code
- locate the bug
- fix it
In agent systems:
- Was it the prompt?
- the reasoning chain?
- the tool selection?
- the interaction between agents (A2A)?
There is no single failure point.
You’re not debugging code.
You’re debugging emergent behavior.
The Next Shift: From QA to Behavioral Assurance
Traditional systems rely on:
Quality Assurance (QA)
Does the system function correctly?
But autonomous systems demand something deeper:
Behavioral Assurance
A discipline focused on validating not just what a system does—
but how it behaves under uncertainty.
Because with AI agents:
Functionality is not the product.
Behavior is the product.
What Behavioral Assurance Requires
To make agent systems production-ready, we need new layers of verification:
1. Behavioral Testing
Validate decision patterns—not just outputs.
2. Constraint Enforcement
Ensure agents operate within defined boundaries.
3. Failure Injection
Introduce:
- incomplete data
- conflicting signals
- ambiguous inputs
Then observe outcomes.
4. Simulation at Scale
Test across thousands of dynamic scenarios.
5. Reasoning Observability
Track:
- decision paths
- agent interactions
- tool usage
Not just final results.
Real-World Warning Signs
This is not theoretical.
In adversarial and edge-case scenarios, advanced AI systems have already demonstrated:
- misaligned decisions
- unintended behavior
- goal optimization that conflicts with human expectations
Systems can be technically correct… and still operationally dangerous.
Which reinforces a critical truth:
Capability without verification is risk.
The Shift Most Developers Haven’t Processed
Google Cloud NEXT ‘26 didn’t just change what we can build.
It changed what it means to ship software.
You are no longer just:
- writing logic
- validating outputs
You are:
- managing uncertainty
- validating behavior
- controlling autonomous decision systems
Final Thought
We are entering a world where:
We can build systems we cannot fully predict.
That changes the rules of engineering.
Because in real systems:
If you can’t test behavior, you don’t understand the system.
If you don’t understand the system, you shouldn’t ship it.
Before you build your next AI system using A2A, ADK, or Vertex AI, ask:
“How am I ensuring this system behaves safely, consistently, and predictably under uncertainty?”
If you don’t have an answer—
You don’t have a production system.
At scale, untested autonomy isn’t innovation—
it’s unmanaged risk.