Agentic AI systems need observability, auditability and ROI discipline

At a glance

What this is: This is Fiddler's analysis of why agentic AI demands a different operating model, with non-determinism, observability and ROI discipline at the centre.

Why it matters: It matters because IAM and security teams increasingly have to govern AI agents as runtime actors whose tool use, data access and decision paths must be auditable, bounded and accountable.

By the numbers:

• 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope.

👉 Read Fiddler's analysis of lessons learned from building agentic systems

Context

Agentic AI changes the governance problem because output is no longer fully deterministic, which makes legacy software assurance models too narrow for production use. In this article, Fiddler frames the shift around observability, auditability, hybrid system design and ROI discipline, all of which matter when AI agents can select actions, tools and timing at runtime.

The identity angle is practical rather than theoretical. When an AI agent can call tools, access data and act across workflows, it starts to behave like a non-human identity that needs scope, traceability and accountability, even if the underlying platform is not yet ready to govern it as one.

The article's starting position is typical of enterprise agentic AI adoption: teams first discover that building something clever is easier than making it governable.

Key questions

Q: How should security teams govern AI models that can call tools and access data?

A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization. The critical shift is to treat every tool call, data read, and update path as a privileged action that can be logged, revalidated, and revoked. Without that discipline, model risk becomes identity risk.

Q: Why do agentic AI systems complicate access control and auditability?

A: They complicate access control because behaviour is probabilistic, not fixed, so the same inputs can lead to different actions. They complicate auditability because teams need evidence of what the agent saw, chose and executed, not just the final answer. That shifts control design toward traceable runtime authority rather than static permission lists.

Q: What breaks when observability is used instead of access control for AI agents?

A: What breaks is the security boundary itself. If teams rely on observability alone, they may see suspicious agent behaviour only after the agent has already accessed data or taken action. The control gap is not detection quality, but the absence of enforceable authorization before execution.

Q: Should organisations prioritise hybrid AI architectures over pure LLM workflows?

A: Yes, where correctness, governance or sensitive data handling matter. Hybrid architectures add validation layers such as rules, knowledge graphs or predictive checks that can constrain unsafe model output before it becomes action. That does not remove risk, but it makes the system easier to govern and less dependent on one model's judgement.

Technical breakdown

Why non-determinism changes agentic AI assurance

Traditional enterprise software assumes repeatable inputs and outputs, which makes validation, regression testing and control design relatively stable. Agentic AI breaks that assumption because the same prompt can produce different actions, tool selections or explanations depending on context and model state. That means assurance has to focus on bounded behaviour, traceability and control layers rather than exact output matching. In practice, the security issue is not just hallucination. It is whether an agent can be constrained, monitored and investigated after it acts.

Practical implication: define runtime guardrails and audit trails before production exposure, not after the first failure.

Why observability becomes a control plane for AI agents

Observability in agentic systems is more than logging. It means capturing tool calls, token usage, decision paths, retrieved context, human overrides and drift over time so investigators can reconstruct what happened. Without that evidence, teams cannot distinguish benign variability from unsafe behaviour or policy violation. For identity and access teams, this is the difference between knowing an agent had permission and knowing how that permission was actually exercised. That evidence is essential for accountability, incident response and compliance.

Practical implication: instrument agent actions as if they were privileged workflows, with immutable records and reviewable traces.

Hybrid architectures reduce blind trust in LLM outputs

The article argues for blending LLMs with predictive models, causal inference and knowledge graphs because pure generative reasoning is fragile in domains that require correctness. Hybrid architectures add verification layers that can validate results, ground claims and reduce the blast radius of model error. From an identity perspective, this matters because access decisions, data retrieval and workflow execution should not depend on a single unconstrained model path. The architecture should make unsafe action harder than safe action.

Practical implication: place verification and policy checks between model output and downstream action, especially where tools or data are sensitive.

Threat narrative

Attacker objective: The objective is to make the agent perform privileged actions, reveal sensitive information or create a governance blind spot that hides unsafe behaviour.

1. Entry begins when an AI agent is given broad tool access or connected to data sources without adequate boundaries or monitoring.
2. Escalation occurs when the agent can chain prompts, retrieval and tool calls into actions that exceed the original human intent or workflow scope.
3. Impact follows when those actions expose sensitive data, trigger unauthorised operations or create investigation gaps because the behaviour was not fully logged.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Meta AI Instagram Account Takeover — 20,225 Instagram accounts hijacked via compromised Meta AI support chatbot with overprivileged access.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Non-determinism is now a governance problem, not just a model property. Once an agent can vary its behaviour at runtime, traditional assurance based on fixed outputs becomes incomplete. The control question changes from whether the model works in testing to whether the system can prove what it did in production. Practitioners should treat variability as a governed risk boundary, not a tuning nuisance.

AI agents are effectively non-human identities with execution privileges. When agents can retrieve data, invoke tools and trigger downstream actions, they need lifecycle governance, scope control and traceability similar to other machine identities. The identity bridge matters because the failure is not only model error, but unmanaged runtime authority. Practitioners should align agent governance with identity and access controls, not only with MLOps.

Observability debt will become AI governance debt: if tool calls, context and decision paths are not captured, the organisation cannot explain, audit or contest the agent's behaviour later. This is a named control gap because it directly converts flexibility into unrecoverable risk. Practitioners should consider missing telemetry a design defect, not an operational inconvenience.

ROI-first adoption will separate durable programmes from speculative pilots. The article is right to push teams away from novelty for novelty's sake, because governability matters more than demo value in production systems. That does not reduce the security burden, it concentrates it on use cases that matter most. Practitioners should prioritise the workflows where failure would create the largest access, data or compliance impact.

Hybrid assurance will define the next phase of enterprise AI controls. Organisations will increasingly combine model outputs with policy checks, retrieval validation and human oversight to reduce blind trust in any single component. That approach does not eliminate risk, but it makes unsafe action harder to execute and easier to detect. Practitioners should evaluate AI systems by the strength of their control chain, not by model sophistication alone.

From our research:

• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to AI Agents: The New Attack Surface report.
• Another finding from our research: 80% of organisations report their AI agents have already performed actions beyond their intended scope, according to AI Agents: The New Attack Surface report.
• Forward look: Ultimate Guide to NHIs , 2025 Outlook and Predictions maps the governance direction as agent populations and machine identities continue to expand.

What this signals

Observability debt will show up as governance debt in the next wave of agentic deployments. Teams that cannot reconstruct agent decisions will struggle to satisfy audit, legal and security review requirements once agents reach sensitive workflows. The practical signal is simple: if you cannot explain the action chain, you do not yet control the system.

AI agents are becoming an identity governance problem as much as an AI engineering problem. As they gain tool access and data reach, they begin to resemble machine identities that need scoping, lifecycle review and revocation discipline. That means identity teams should join agent design reviews early, not after production drift starts.

The most resilient programmes will measure whether guardrails still work under real workload pressure, not whether the demo looked controlled. That makes logging, exception handling and approval boundaries central to operational readiness.

For practitioners

• Instrument agent runtime telemetry end to end Capture tool calls, retrieved context, token usage, approvals and overrides so every meaningful action can be reconstructed after the fact. Treat missing traces as a control failure, especially where agents can reach sensitive systems or data.
• Bind agent permissions to explicit task scope Limit each agent to the minimum data, tools and downstream actions needed for a named workflow, then revoke or narrow access when the task ends. Use this to reduce the chance that a successful prompt becomes broad operational authority.
• Insert verification before action execution Require policy checks, retrieval validation or human approval before high-impact actions such as data export, configuration change or external communication. This keeps model output from becoming an unchecked instruction stream.
• Rank use cases by governance impact, not novelty Start with workflows where bad agent behaviour would create the most material access, data or compliance exposure. That forces design choices around auditability, containment and accountable ownership from the outset.

Key takeaways

• Agentic AI breaks the deterministic assumptions that traditional software testing relies on, so governance has to move to runtime controls.
• Auditability, observability and task-scoped access are the practical controls that determine whether AI agents can be investigated and contained.
• For IAM and security teams, AI agents should be treated as governed runtime identities, not as untouchable application features.

Key terms

• Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions — including calling APIs, writing code, and orchestrating other agents — with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
• Observability: Observability is the ability to understand the internal state of a system from the data it produces. In security and operations, that means combining logs, metrics, and traces so teams can explain why something happened, not just confirm that something changed.
• Hybrid Architecture: A hybrid architecture combines LLMs with other control or reasoning components such as rules, retrieval, predictive models or knowledge graphs. The purpose is to validate outputs, constrain unsafe actions and reduce reliance on a single probabilistic system for high-impact decisions.
• Task-Scoped Access: Task-scoped access is permission granted for one defined purpose and removed once the task is complete or the session expires. For non-human identities, it reduces standing privilege and limits how long an attacker can exploit a stolen credential.

What's in the full article

Fiddler's full blog covers the operational detail this post intentionally leaves for the source:

• Examples of how the team structures agent observability across tool calls, token usage and workflow traces
• Discussion of ROI-led prioritisation for agentic use cases, including which workloads to pilot first
• Practical guidance on hybrid architectures that combine LLMs with predictive and causal validation
• The article's framing on how collaboration between business and technical stakeholders changes adoption decisions

👉 The full Fiddler post expands on observability, ROI discipline and hybrid architecture choices for agentic deployments.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security and secrets management. It is designed for practitioners who need to connect identity controls to emerging AI and automation risks.