Beyond accuracy: Architecting performant, cost-efficient agentic AI

When enterprise teams evaluate AI systems, the conversation almost always starts and ends with accuracy.

Today, organizations everywhere are experimenting with AI agents to build workflow automations, copilots, and customer service tools. Initially, these proof-of-concept (POC) systems work incredibly well because they solve narrow, tightly defined problems.

But as AI moves out of the sandbox and into enterprise production, accuracy alone isn't enough. Agentic systems must also be performant, scalable, and cost-efficient. Let's look at why architecture matters more than the model you choose, and how you can optimize your AI systems for the real world.

The core engine: The reason-act loop

Before we can architect better AI systems, we need to understand how enterprise agents actually execute tasks. Most rely on a foundational "reason-act" loop:

• Thought: The agent analyzes the goal and reasons about the problem using its current context and memory.
• Action: It selects a specific action or tool to execute.
• Observation: It observes the result from the tool and updates its memory.
• Repeat: The cycle repeats until the agent completes the task.

This structure gives agents the power to handle complex, multi-step workflows. But it also introduces a critical architectural vulnerability. The more context, memory, and tools we inject into this continuous loop, the more expensive and inefficient the system becomes.

The "mega prompt" trap: Why more context =/= better outcomes

In the rush to deliver value, teams often choose the fastest architectural path. As they expand an agent's capabilities, they start passing more tools, more integrations, and more grounding data into the workflow.

Let's look at a practical field service example. Imagine a technician reports that a main conveyor motor is overheating and needs a replacement fan.

A standard, monolithic architecture, or what we call the mega prompt trap, tries to solve this by gathering everything at once. It takes the transcribed audio, pulls all historical fault reports from the Field Service Management (FSM) system, grabs the CAD manuals, fetches the parts inventory from the ERP, and dumps it all into a single, massive context window for the agent to process.

These monolithic designs rarely show their flaws during a prototype. But as enterprise context grows, they trigger hidden scaling emergencies:

• Excessive grounding: The agent processes massive amounts of irrelevant information during every reasoning cycle.
• Latency growth: More processing overhead means slower response times for your users.
• Token explosion: API costs skyrocket as the context window swells.

More context doesn't equal better outcomes. In fact, it actively degrades your architecture.

Architecture > model choice: Optimized grounding

When AI starts hallucinating or slowing down, it's tempting to blame the LLM or upgrade to a newer, more expensive version. However, the biggest drivers of AI scalability, performance, and cost are usually architectural decisions, not the model itself.

To fix the scaling issues in our field-service scenario, we implemented an optimized grounding architecture. Instead of pushing all enterprise context into a single centralized agent, we introduced modular, targeted retrieval patterns. We gave the architecture specialized tools like AISearchService to retrieve only the information required for a specific task.

For example, the agent analyzes the fault report and the repair procedures to formulate the required parts. However, checking the ERP inventory for stock configurations happens completely outside the agent's core cognitive scope.

The real-world impact of good architecture

When we tested both architectures side-by-side, scaling up from 100 to 1,000 reports, the impact of limiting grounding context was impressive.

As the data shows, while both architectures solve the exact same business problem, the optimized grounding approach scales better:

• Latency: Cut nearly in half (dropping from 15 seconds to just 8 seconds).
• Token spend: Dropped significantly from 413K tokens down to just 84K tokens per workflow call.
• Hallucination rate: Dropped from 20% down to a reliable 2–5%.

Measure AI like any other enterprise system

Improving your architecture only matters if you can measure the impact objectively. If you want to build reliable AI solutions at scale, you have to treat AI exactly like any other enterprise system, using observability, evaluations, and operational metrics.

With tools like OutSystems Developer Cloud (ODC) Portal Analytics, architects can monitor execution performance across the entire agent pipeline.

When evaluating your systems, ensure you track these three vital metrics:

• Latency: Monitor response times and execution trends. Latency in AI is rarely caused by a single model call; it’s usually the result of orchestration overhead and inefficient retrieval patterns.
• Token spend: Track tokens at the individual agent level, but more importantly, measure the workflow token usage by aggregating consumption across the entire pipeline. This tells the true story of your operational costs.
• Hallucinations (Agent Evaluations): Use a platform judge and a fixed dataset of test cases to measure how often the system produces incorrect or unsupported outputs that require human intervention.

The bottom line

Building enterprise-grade AI is a balancing act. It requires moving beyond the initial excitement of accuracy and focusing on the strict mechanics of scale. Keep your context tightly controlled, modularize your agent's responsibilities, and measure everything.

When you get the architecture right, performance and cost-efficiency will naturally follow.

Ready to dive deeper? Catch the on-demand OutSystems ONE session that sparked this article, and discover exactly how to engineer and measure high-performance agentic systems at enterprise scale.

Rushikesh Kulkarni has spent the past decade in technology, from writing code to architecting solutions to leading teams. What he enjoys most is helping customers solve real business and IT challenges, turning a messy problem into something that actually works. Today he leads a team of Technical Leads at OutSystems, and he's a regular speaker at OutSystems ONE and Developer Days, where he digs into what it really takes to build agentic AI systems that hold up in production.

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