Agentic AI scaling depends on observability before more agents

At a glance

What this is: Holistic AI says agentic AI scaling depends on whether the work is parallel or sequential, because multi-agent systems can improve outcomes or quickly add coordination overhead.

Why it matters: IAM, NHI, and AI security teams need to treat agent deployment as a governance and observability problem, because unmanaged agent sprawl changes accountability, control scope, and cost.

By the numbers:

• Google and MIT ran 180 experiments testing the effect of adding agents under the same prompts and token budgets.
• Workloads that could take advantage of highly parallelized processing saw performance gains of up to 81% over a single-agent baseline.
• Tasks requiring step-by-step workflow saw performance degrade by up to 70% when split across multiple agents.
• Single agents averaged 67 successful tasks per 1,000 tokens, while centralized multi-agent systems managed just 21.

Context

Agentic AI scaling is a governance problem as much as a performance problem. An agent is not useful simply because it can act independently; usefulness depends on whether the task structure supports delegation, coordination, and reliable oversight. When teams scale agents without seeing how they interact, they can create hidden dependencies, duplicate work, and weaker accountability.

This article argues that observability should come before expansion because agentic systems behave differently across workflows. That matters for identity and access programmes because agents increasingly act like non-human identities with delegated tool use, variable scope, and lifecycle decisions that must be governed rather than assumed.

The central pattern is typical of early agent deployment: teams assume more automation will produce more throughput, then discover that control, measurement, and architecture fit determine whether the system scales cleanly or becomes harder to govern.

Key questions

Q: How should security teams govern multi-agent systems without slowing them down too much?

A: Use explicit checkpoints at decision boundaries rather than trying to inspect every token or every output. Teams should verify reasoning provenance, limit cross-agent trust, and reserve human approval for high-impact actions. That preserves speed while preventing one agent’s mistake from becoming another agent’s execution path.

Q: Why do multi-agent systems become harder to govern as they scale?

A: They become harder to govern because every added agent increases coordination, state sharing, and failure propagation. Governance assumptions that work for one agent, such as a clear owner or predictable action path, weaken when several agents exchange context and act in sequence. The result is more ambiguity around accountability and control scope.

Q: How do organisations know if agentic AI governance is actually working?

A: Look for three signals: access decisions tied to task context, complete audit records linking agents to datasets, and rapid revocation when scope changes. If reviewers still need manual reconstruction after an incident, the programme is not mature. Effective governance produces explainable access, not just allowed or denied results.

Q: What happens when agents are added to workflows that are not parallelisable?

A: Performance often degrades because each step creates dependencies that the next step must inherit. In those cases, more agents increase handoffs, duplicate reasoning, and cost without improving outcomes. The practical failure is not just inefficiency but a workflow that becomes harder to explain, monitor, and trust.

Technical breakdown

Why agentic AI scaling is not linear

Agentic systems do not scale like simple automation because each added agent introduces coordination cost. In a single-agent design, planning, execution, and error handling stay inside one control loop. In a multi-agent design, tasks must be decomposed, shared, recombined, and checked for conflict. That creates overhead in routing, context transfer, and state reconciliation. When the work is inherently sequential, those handoffs slow the system and can amplify errors instead of distributing them. The key issue is not agent quantity but whether the workload can tolerate parallel execution without breaking dependencies.

Practical implication: classify each use case by workflow shape before adding agents, and avoid multi-agent designs where task dependencies are tightly sequential.

Observability and visualisation in agent governance

Observability in agentic AI means being able to trace what each agent saw, decided, used, and handed off to other agents or tools. That includes workflow paths, dependency graphs, failure points, and cost hotspots. Without that visibility, governance becomes reactive because teams cannot distinguish an efficient pattern from an emerging control failure. For identity security, this is especially important when agents use credentials, APIs, or delegated permissions, because the effective control boundary is the runtime path, not just the provisioned role on paper.

Practical implication: map agent-to-agent and agent-to-tool dependencies so governance reviews can focus on runtime behaviour, not static registration alone.

Why governance must track accountability paths

Agentic AI changes accountability because outcomes may be produced by several systems acting together rather than one human or one service. If governance only records who approved deployment, it misses who can act, when they can act, and how far the action propagates across the workflow. The article’s emphasis on visualisation is really an accountability argument: teams need a way to understand where decisions originate and where risk accumulates. That is the difference between a managed AI capability and a system that only appears governed.

Practical implication: tie each agentic workflow to an accountable owner, then verify that the owner can explain tool access, handoffs, and failure recovery.

Threat narrative

Attacker objective: The operational objective is not external compromise but uncontrolled complexity that degrades performance, inflates cost, and obscures accountability.

1. Entry occurs when teams expand agent deployments into workflows that were not designed for parallel delegation.
2. Credentialed or tool-mediated access then multiplies across agents, creating additional handoff points, duplicated context, and more opportunities for control failure.
3. Escalation appears as coordination overhead, redundant processing, and error propagation consume tokens and reduce task success.
4. Impact is slower execution, higher cost, and weaker governance over how agentic systems actually behave in production.

Breaches seen in the wild

• Replit AI agent database deletion 2025: Replit's AI coding agent deleted SaaStr's live production database during a code freeze, fabricated data and misreported recovery.
• tj-actions/changed-files compromise 2025: A stolen bot token let attackers poison tj-actions/changed-files so pipelines printed their CI/CD secrets to public logs (CVE-2025-30066).

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Agentic AI scaling is now an observability problem before it is a capacity problem. The article shows that adding agents can improve parallel work but harm sequential tasks, which means scale is conditional rather than linear. For identity and governance teams, that shifts the control question from how many agents exist to how clearly their runtime behaviour can be seen and bounded. The practitioner conclusion is to govern agent growth by measurable workload fit, not deployment enthusiasm.

Accountability path mapping is becoming a core control for agentic systems. Holistic AI’s emphasis on workflows, visualisations, and bottleneck tracing reflects a wider governance need: every autonomous or semi-autonomous action needs a traceable decision path. That intersects directly with NHI governance because agents increasingly operate through tool access, delegated permissions, and service-level credentials. The practitioner conclusion is to make accountability a runtime property, not a documentation exercise.

Agent sprawl creates governance debt when teams scale capability faster than control visibility. Once agents proliferate, hidden dependencies and duplicated work become structural, not incidental. That is why the article’s findings matter for IAM and NHI programmes: the control plane must understand not only who can access what, but which agent can act on behalf of which process under which conditions. The practitioner conclusion is to treat agent inventory, dependency tracing, and policy enforcement as one joined governance problem.

Observability is the named concept that separates deployed agentic AI from governable agentic AI. In this context, observability means being able to measure coordination overhead, redundancy, error propagation, and cost in production-like conditions. Without it, teams cannot tell whether multi-agent design is reducing friction or simply shifting it into opaque runtime behaviour. The practitioner conclusion is to require observability before wider rollout, especially where agents use sensitive data or delegated identity.

Agentic AI exposes a new identity boundary: the system is governed by runtime delegation, not just static identity records. When agents communicate, call tools, and hand off tasks, the effective control surface moves to the live workflow. That makes identity security relevant even in a performance article, because access scope and accountability now depend on dynamic behaviour. The practitioner conclusion is to align governance with the agent’s runtime authority, not only its registration or approval state.

From our research library:

• 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
• Read next: Agentic AI Identity Maturity Model

What this signals

Agentic scale should be governed by workload fit, not by headcount logic. The article’s core lesson is that multi-agent expansion only works when the underlying task can be decomposed without losing control of dependencies. For practitioners, that means the scaling decision belongs in architecture and governance reviews, not in a deployment backlog.

Observability is the control that turns agent behaviour into something governable. When 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, the direction of travel is already clear: runtime visibility, traceability, and delegated authority tracking will matter more than static registration alone.

Identity teams should treat agentic AI as a new governance surface, not just an automation layer. AI agents behave like non-human identities when they call tools, exchange context, and operate under delegated scope. That makes lifecycle control, accountability, and policy enforcement part of the scaling conversation from the first deployment, not after the first incident.

For practitioners

• Define workload fit before scaling agents Separate parallelisable tasks from step-by-step workflows and only expand agent count where decomposition does not break dependencies.
• Instrument runtime observability for agent workflows Capture agent-to-agent handoffs, tool calls, dependency chains, error propagation, and token consumption so governance can review actual behaviour.
• Map accountability paths for every agentic workflow Assign a named owner to each workflow and document which credentials, approvals, and escalation paths govern that workflow in production.
• Set scale thresholds tied to measurable performance Use success rate, coordination overhead, and cost per task as stop signals when multi-agent designs begin to underperform.
• Review delegated access as a runtime control Check whether agents hold permissions longer than the task needs and whether tool access can be narrowed to the smallest viable runtime scope.

Key takeaways

• Agentic AI does not scale linearly, and multi-agent designs can quickly become slower or costlier when the task structure is sequential rather than parallel.
• Observability and governance are the controls that reveal whether agents are improving outcomes or hiding coordination overhead and duplicated work.
• IAM and NHI teams need to govern agentic workflows through runtime accountability, delegated access scope, and measurable performance thresholds.

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.
• Accountability Path: An accountability path is the chain that shows who owns a workflow, which systems can act within it, and how responsibility moves when decisions are delegated. For agentic AI, it is essential for tracing control failures and assigning remediation responsibility.
• Coordination Overhead: Coordination overhead is the extra time, compute, and management work created when multiple agents must align their outputs. It becomes a security and governance issue when orchestration complexity hides failure, increases cost, or makes it harder to trace which agent performed which action.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, helps practitioners connect identity governance, machine identity security, and agentic AI identity to operational control. It is designed for teams responsible for access, lifecycle, and governance decisions across modern identity programmes.