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Agentic AI Challenges

Where Agentic AI Cost
and Performance Go Wrong

Autonomous agents introduce non-deterministic behavior, nonlinear cost growth, and unpredictable performance that traditional FinOps, monitoring, and capacity-planning approaches cannot manage.

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Organizations struggle to choose the most appropriate agentic AI platform and determine the minimum required configuration that meets business performance goals—without overprovisioning or exceeding budget constraints.

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Agentic AI systems operate across multiple layers—agents, orchestration, data platforms, cloud infrastructure, and external services. Achieving unified observability and proactive performance management across these complex, distributed environments remains a major challenge.

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Accurately sizing new agents and agentic AI applications before production is difficult due to unpredictable workloads, concurrency, recursion, and dynamic agent interactions—often leading to performance risk or unnecessary cloud spend.

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Managing capacity dynamically as workloads fluctuate—and making informed cloud migration decisions—requires continuous analysis of cost, performance, and utilization across platforms, regions, and deployment models.

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As agentic AI workloads grow and change over time, organizations must continuously manage performance and cost to prevent drift, performance degradation, and financial surprises.

Why Agentic AI Is Hard to Operate at Scale

Autonomous agents introduce non-deterministic behavior, nonlinear cost growth, and unpredictable performance that traditional FinOps, monitoring, and capacity-planning approaches cannot manage.

  • Platform & Budget Selection
  • Choosing the right agentic AI platform and the minimum configuration to meet performance goals—without overspending or guesswork.
  • End-to-End Observability
  • Achieving unified observability and proactive performance management across agents, orchestration, data platforms, and hybrid multi-cloud infrastructure.
  • Pre-Production Sizing
  • Accurately sizing new agents and agentic AI applications before production to avoid performance risk and cloud cost overruns.
  • Dynamic Capacity Management
  • Continuously adapting capacity as workloads fluctuate—while optimizing cloud placement and migration decisions.
  • Continuous Cost and Performance Control as Workloads Evolve
  • As agentic AI workloads grow and change over time, organizations must continuously manage performance and cost to prevent drift, performance degradation, and financial surprises.

Challenges of Planning and Managing Agentic AI Systems

Agentic AI systems introduce a new level of autonomy, complexity, and uncertainty into enterprise environments. Unlike traditional applications, agentic systems consist of multiple autonomous agents that reason, interact, trigger downstream actions, and continuously adapt.

Planning their deployment and managing performance and cost over time therefore involves far greater decision risk.

  • Decisions Under Uncertainty

Early in the lifecycle, organizations must select platforms, cloud services, configurations, and budgets without fully understanding how agents will behave in production. A single business request can generate multiple LLM calls, vector searches, tool invocations, retries, and recursive agent interactions. These behaviors are highly workload-dependent and often nonlinear, making accurate estimation difficult. Overprovisioning wastes budget, while underprovisioning risks performance degradation and missed service-level goals.

  • Planning Without Predictable Workloads

Traditional capacity planning assumes stable workloads. Agentic AI breaks this assumption. Agent activity fluctuates based on user behavior, data availability, orchestration logic, and interactions between agents. Agents can trigger other agents or enter feedback loops, causing rapid and unexpected increases in resource consumption. Sizing new agents and applications before production therefore becomes a high-risk exercise without workload-aware forecasting and validation.

  • Multi-Layer Complexity and Limited Visibility

Agentic AI systems span multiple layers—agents, orchestration frameworks, LLMs, vector stores, data platforms, and cloud infrastructure. Performance and cost issues rarely originate from a single component; they emerge from interactions across layers. Limited observability makes it difficult to identify root causes, slowing response and increasing operational risk.

  • Ongoing Risk of Cost and Performance Drift

Even after deployment, agentic AI workloads continue to evolve. New agents, changing data, and growing usage can quickly invalidate original assumptions. Without continuous monitoring and control, systems drift away from performance targets and budget expectations, leading to financial surprises and loss of stakeholder trust.

The central challenge is decision risk: making high-impact planning and operational decisions without reliable, verifiable insight into how agentic AI systems actually behave at scale.