How organisations build confidence in autonomous behaviour

This blog is part of a series.

Read previous part: Tooling Architecture for Agentic Systems

Agentic systems change what confidence needs to mean.

They do not simply produce outputs. They make decisions, take actions, and operate across systems over time. Confidence in their behaviour cannot be inferred from initial performance or a small number of successful test cases. It has to be built deliberately.  Testing is central to that process, but it is not sufficient on its own.

Confidence in an agentic system comes from a combination of structured testing, visibility into behaviour, and the ability to detect and respond to failure. The approaches that provide confidence in conventional software do not transfer cleanly to systems that are non-deterministic, long-horizon, and capable of acting on live systems.

As a result, organisations need to think more carefully about what testing is intended to achieve and what confidence means in this context.

Why agent testing is different

In conventional software testing, the goal is to verify that a defined input produces a defined output. Tests are repeatable, failures are deterministic, and coverage can be measured with reasonable precision. A sufficiently comprehensive test suite provides a high degree of confidence that the system behaves as intended.

These properties do not hold cleanly for agentic systems.

The same input given to an agent on two separate occasions may produce different reasoning, different tool calls, and different outputs. Neither response is necessarily incorrect; the system is operating as designed. However, this means that a test that passes once cannot be assumed to pass consistently, and a failure that appears once may not be straightforward to reproduce.

Agentic tasks are also long-horizon in a way that conventional tests are not. An agent working through a complex workflow may take many steps before producing a final output. An error early in the process may not become visible until much later. Testing that focuses only on final outputs misses the accumulation of small decisions that shape the overall outcome.

In addition, agents act through tools on live systems. Failures are therefore not limited to incorrect outputs. They can take the form of incorrect actions: records updated inappropriately, communications sent in error, or workflows triggered unintentionally. The consequences of a testing gap are not confined to a test environment. They can have direct effects in production systems.

The components of an agentic testing framework

A testing framework for agentic systems needs to reflect how these systems behave in practice. It must account for variation in outputs, the sequencing of actions, and the interaction between reasoning and external systems.

• Adversarial and ambiguous task construction
  Agents will encounter inputs that are unclear, incomplete, or contradictory. Testing only with well-formed inputs produces systems that perform reliably within a narrow range of conditions but behave unpredictably outside it. A robust testing approach includes tasks that introduce ambiguity, missing information, and conflicting constraints, in order to expose failure modes that straightforward test cases will not reveal.
• Long-horizon testing
  Agent behaviour needs to be evaluated across the full length of a task, not only at the point where an output is produced. This involves tracing the agent’s reasoning and actions step by step, assessing the quality of intermediate decisions, and ensuring that the task remains coherent as it progresses. Without this, early errors may go undetected until they have compounded into more significant failures.
• Tool chain simulation
  Agents should be tested against realistic tool environments without interacting with production systems. Simulated tools can replicate the interfaces, response patterns, and occasional failures of real systems. This makes it possible to observe how agents respond to unexpected conditions without risking live data or triggering unintended actions.
• Replay systems and reproducibility
  Non-deterministic behaviour makes debugging difficult unless execution can be reproduced. When an agent produces an unexpected result, it is important to reconstruct the conditions that led to it, including inputs, retrieved information, tool responses, and intermediate steps. Without this capability, diagnosing failures becomes uncertain. With it, the reasoning process can be examined in detail and the source of the issue identified.
• Behaviour traces and decision analysis
  Testing requires visibility into how decisions are made. Behaviour traces record the sequence of actions taken by the agent and the information it used at each step. Comparing traces across runs makes it possible to understand where behaviour varies and whether that variation is acceptable. This provides insight that cannot be obtained from simple pass or fail outcomes.
• Invariant checks and regression tests
  Certain properties must hold regardless of how the agent approaches a task. These may include safety constraints, quality requirements, or domain-specific rules. Invariant checks verify that these properties are consistently upheld, while regression tests ensure that previously identified issues do not reappear.

In practice – Building confidence in agentic systems

Confidence in agentic systems is not established through testing alone. It is built through a combination of practices that reinforce one another.

Testing provides evidence that the system behaves as expected across a range of scenarios. Observability provides visibility into how the system behaves in practice. Replay and traceability make failures diagnosable. Guardrails and invariant checks ensure that critical properties are maintained regardless of how the agent operates.

Each of these elements addresses a different aspect of the problem.

Testing without visibility makes it difficult to understand why failures occur. Visibility without structured testing provides insight but not assurance. Guardrails without testing may prevent certain classes of failure but do not establish confidence in overall behaviour.

Taken together, these practices create a system in which behaviour can be observed, evaluated, and improved over time. Confidence develops not because the system is free of error, but because its behaviour is understood and its failures are manageable.

The Maturity of Testing Practice

Testing practices for agentic systems are still developing.

The tools and frameworks that support conventional software testing have evolved over many years. Equivalent infrastructure for agentic systems is less mature, and organisations are often defining their own approaches as they build and deploy these systems.

This does not reduce the importance of testing. If anything, it increases it.

Testing needs to be treated as a core part of the engineering effort, not as an activity that follows once development is complete. Organisations that succeed in deploying reliable agentic systems are those that invest early in building testing capability and develop the discipline to apply it consistently.

What confidence in an agentic system actually looks like

Confidence in an agentic system is not the absence of known failures.

It is a well-founded belief, based on systematic evidence, that the system behaves reliably across the conditions it will encounter in production, and that when it does fail, the failure can be detected, understood, and corrected.

Confidence cannot be assumed, and it cannot be inferred from good performance on simple test cases. It has to be established through deliberate and sustained effort.

For business leaders, the relevant question is not whether a system has been tested. It is whether the testing approach is sufficient to surface problems before they emerge in production.

Learn more about Bell’s AI Solutions.

Meet the Author:

Alison Hodivala
Director of AI & Data Portfolio Strategy at Bell Integration

Alison Hodivala is the Director of AI & Data Portfolio Strategy at Bell Integration, where she focuses on ensuring the company’s AI and data services reflect market needs and are supported by delivery models that scale reliably. She works across product, delivery and commercial teams to develop offerings that are practical, relevant and capable of delivering business impact.

Her career spans more than 25 years in start-ups and global organisations, covering consultative sales and delivery, enablement and change management. Drawing on a background in organisational psychology, Alison applies an evidence-based approach to AI adoption, aligning people, processes and technology to achieve effective and responsible outcomes.