Explainability

What is explainability in agentic systems?

Explainability is the ability to clearly describe why an agent made a specific decision or took a particular action.

In agentic systems, this includes translating policy evaluation, delegation, tool selection, and constraints into explanations that humans can understand.

Explainability ensures agent behavior is not opaque, even when actions are autonomous.

How do we generate human-readable decision explanations?

Human-readable explanations are generated by linking actions to identity, policy, and context.

Each decision is explained using:

• The agent identity

• The permissions and scope in effect

• The policies evaluated

• The data or signals that influenced the outcome

This allows non-technical stakeholders to understand decisions without inspecting raw logs.

Can we replay a past decision to understand why it happened?

Yes. Decision replay is possible when inputs, context, policies, and state are captured at decision time.

Replaying a decision evaluates the same conditions again to reconstruct how and why the outcome occurred.

Replay is essential for audits, incident investigations, and regulatory review.

What is “what-if” analysis in agentic decision-making?

What-if analysis evaluates how a decision would have changed under different conditions.

By modifying inputs, permissions, or policies, organizations can test alternative outcomes without affecting production systems.

This is useful for validating policies, improving safety controls, and training teams.

How do we constrain agents to a safe “Tool Catalog”?

Agents are constrained to a safe tool catalog by explicitly defining which tools are allowed, for what purpose, and under what conditions.

Only cataloged tools can be invoked, and every invocation is checked against policy.

This prevents agents from dynamically discovering or using unsafe or unauthorized tools.

How do we ensure “Separation of Duties” for agent workflows?

Separation of Duties is enforced by splitting sensitive workflows across multiple agents or approval steps.

No single agent is allowed to initiate, approve, and execute a high-risk action alone.

This reduces the risk of abuse, errors, or silent escalation in autonomous workflows.

How does explainability support trust and governance?

Explainability allows organizations to demonstrate control, intent, and accountability.

When decisions can be explained clearly, agents become governable rather than opaque.

This is critical for internal governance, customer trust, and regulatory compliance.

Why is explainability essential for regulated environments?

Regulators often require organizations to explain why access was granted, why an action occurred, or why data was used.

Explainability provides defensible answers that go beyond technical logs.

In agentic systems, explainability is necessary to justify autonomous behavior.