Series: The Rise of Agentic Operations|Sub-series: Building the Agentic Enterprise

TL;DR — Key Takeaways

1. Global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027. The gap shows up long after the budget is set.
2. The four layers that matter most -- orchestration, model routing, execution logging, and data normalization -- are almost never designed for upfront.
3. Data preparation alone accounts for 40 to 60% of total AI project effort. It is usually the largest single cost in the first month of a real build.
4. Most organizations learn what infrastructure they needed by building without it first. The ones that get it right decide on architecture before they decide on models.
5. This blog is part of our Building the Agentic Enterprise series on what it actually takes to deploy AI agents at scale.

Most teams start an AI agent project by picking a model. They evaluate LLMs, compare capabilities, run some prompts, and get excited. The build begins.

Three months later, the system works in the demo and falls apart in production. Not because the model was wrong, but because everything underneath it was never designed for real use.

This is the pattern behind 80% of enterprises missing their AI infrastructure forecasts by more than 25%. The technology gets chosen before the infrastructure gets designed. And the infrastructure is where most of the actual cost, risk, and complexity lives.

A solid AI agent deployment framework starts with the layers below the model, not the model itself. If you are still working through whether your organization is ready to build, The Rise of Agentic Operations covers the case for why this matters now.

What infrastructure does an AI agent actually need?

An AI agent needs four layers to function reliably in production: orchestration, intelligent model routing, execution logging, and a clean data pipeline. Most teams build none of these before they start. All of them become urgent once the system is live.

Orchestration is the layer that sequences tasks, manages handoffs between agents, and handles retries and failures. The AI orchestration market was valued at $5.8 billion in 2024 and is projected to reach $48.7 billion by 2034 -- that growth is a direct signal of how many organizations have learned the hard way that agents without orchestration do not hold up. When something goes wrong at 2 AM, orchestration is what tells you exactly what happened and why.

Intelligent model routing is the layer most teams skip entirely. Not every task needs the same model. High-stakes judgment calls -- risk assessment, claims validation, compliance review -- deserve a capable model. Fast, structured, repeatable tasks can use something lighter and cheaper. RouteLLM demonstrated a 2x cost reduction while maintaining 95% of GPT-4 quality by routing tasks to the right model based on their characteristics. That is not just cost optimization. It is a quality and latency decision that compounds at scale.

Execution logging is what makes the system debuggable, auditable, and improvable. Every agent run needs to log inputs, outputs, tokens consumed, cost incurred, and time elapsed. Without it, you cannot explain what the agent did, cannot optimize how it performs, and cannot prove to stakeholders that it is working. This is not a nice-to-have. It is the foundation of accountability.

Data normalization is the unglamorous layer that almost always surfaces as the biggest upfront cost. Craig Taylor, Practice Lead at Ciberspring, encountered this building a formulary-parsing agent for a pharma client. The agent needed to read payer documents -- but those documents varied wildly in format, structure, and terminology across different insurers. The data had to be standardized before any model logic could run. As Craig described it:

Why do organizations consistently underestimate AI infrastructure costs?

The short answer: they plan for the model and forget everything else. Transitioning from proof of concept to production increases total investment by 250 to 400%. That multiplier surprises almost every team that has not built a production AI system before.

Average monthly AI spend grew 36% in a single year, from $62,964 in 2024 to $85,521 in 2025. Organizations spending more than $100,000 per month doubled from 20% to 45% in that same period. The costs that drive this growth are not the model costs teams budget for. They are the infrastructure costs teams discover after the build has started.

Data preparation alone accounts for 40 to 60% of total AI project effort according to IBM. For complex initiatives, it can consume 80% of total project time. This is the work of extracting, cleaning, and normalizing the data that agents will actually use. It is also the work most teams assume will be straightforward until they look at their actual data.

Poor data quality costs the average organization $12.9 million per year according to Gartner. That figure understates the cost for organizations trying to run AI agents on messy data, because bad data does not just slow the system down. It produces bad outputs, which erodes trust faster than almost anything else.

Unity Software learned this in 2022 when bad data from a single customer caused $110 million in lost revenue and a $4.2 billion market cap loss. The AI system worked exactly as designed. It was designed on faulty data.

Want to understand what your build will actually cost before you start? Talk to our team →

What does an AI agent deployment framework actually look like in practice?

Craig's approach to scoping an AI deployment starts with what he calls workflow decomposition. Before any infrastructure is specced, the workflow gets broken into its discrete steps. Each step gets evaluated independently: what kind of input does it receive, what decision or action does it produce, how often does it run, and what happens when it fails.

That decomposition determines the infrastructure requirements. Some steps are well-suited for LLMs. Others need structured rule engines. Some need a human in the loop. Trying to use the same model for everything, or the same architecture for every step, is where cost and quality problems originate.

The orchestration layer then gets designed around what the decomposition reveals -- not around what the vendor recommends. 29% of organizations have already adopted agentic AI, with another 44% planning to within 12 months. The ones who will get real value from it are the ones designing orchestration for their specific workflow characteristics, not buying a platform and hoping the workflows fit.

Model routing follows. Once the workflow steps are mapped, the right model for each step becomes a cost and quality decision rather than a guess. High-complexity steps get a capable model. High-volume, low-complexity steps get a cheaper one. That routing architecture is documented, version-controlled, and adjustable as costs and model capabilities change.

Execution logging goes in from day one. 75% of organizations are increasing their observability budgets, and AI capabilities are now the top criterion for selecting an observability solution. The teams increasing those budgets are the ones who deployed without observability first and learned what they were missing.

What happens when organizations skip the infrastructure layer?

They scale a system that was never designed to scale. More than 55% of all enterprise data qualifies as dark data, costing organizations $1.7 to $3.3 million annually just to store and manage. When an AI agent is built on top of that data without a normalization layer, every output carries the quality problems of the underlying data. The agent does not fix bad data. It amplifies it.

53% of organizations reported skills gaps or staffing shortages for managing AI computing infrastructure, and 42% pulled an AI workload back from public cloud due to data privacy or security concerns. Both of those problems are infrastructure problems, not model problems. They do not get solved by picking a better LLM.

Craig is direct about the sequencing:

This is the case for designing the infrastructure layer before choosing the model layer. LLM costs have dropped roughly 10x annually and will continue to fall. The architecture decisions -- orchestration, routing, logging, data -- are the ones that compound over time. Getting those right is what separates a working production system from an expensive demo.

Ready to design the infrastructure layer before the build starts? Let’s talk →

FAQ

What is an AI agent deployment framework?

An AI agent deployment framework is the set of infrastructure, architectural decisions, and operational processes that allow AI agents to run reliably in production. It covers orchestration, model routing, execution logging, data normalization, error handling, and human oversight checkpoints. The model is one component. The framework is everything around it.

What does orchestration do in an AI agent deployment?

Orchestration sequences tasks between agents, manages handoffs, handles retries when steps fail, and provides the traceability needed to debug unexpected behavior. The AI orchestration market is projected to reach $48.7 billion by 2034 -- that growth reflects how many organizations have discovered they needed orchestration after building without it.

Why is data preparation such a large cost in AI projects?

Data preparation accounts for 40 to 60% of total AI project effort because most enterprise data is not in a format that AI agents can use. Documents, legacy system outputs, unstructured files, and inconsistent schemas all require extraction, cleaning, and normalization before any model logic runs. This work is usually the largest single cost in the first month of a real build.

What is intelligent model routing and why does it matter?

Model routing sends different tasks to different models based on their complexity and risk level. High-stakes tasks go to more capable models. High-volume, low-complexity tasks go to cheaper ones. RouteLLM showed a 2x cost reduction while maintaining 95% of GPT-4 quality by routing intelligently rather than sending everything to the same model. At scale, the cost and latency savings are significant.

Why do AI infrastructure costs surprise organizations?

Because teams plan for model costs and underestimate everything else. Moving from proof of concept to production increases total investment by 250 to 400%. The costs that drive that multiplier are infrastructure costs: data pipelines, orchestration, logging, and multi-tenancy architecture. These are rarely in the original budget because they were not designed for in the original plan.

What should be designed before choosing an AI model?

The workflow decomposition should come first -- breaking the target process into discrete steps and understanding what each step requires. From that, the orchestration architecture, model routing logic, logging requirements, and data pipeline scope all become clearer. The model selection follows from those decisions, not the other way around.

Up next in this series

How to Get Your Team to Actually Trust AI Agents

The tools are ready. The harder part is getting the people who use them to trust them. The next blog covers shadow mode deployment, incremental autonomy, and how to reframe the conversation for teams who feel like agents are replacing them.

Read it when it publishes →

Part of: The Rise of Agentic Operations |Sub-series: Building the Agentic Enterprise