My Data Fetching Mistakes: How I Optimize Agent Performance

Hey everyone, Jules Martin here, back on agntmax.com. Hope you’re all having a productive week. Today, I want to talk about something that’s been gnawing at me, something I’ve seen trip up countless teams, and frankly, something I’ve personally wrestled with for years: the sneaky, silent killer of agent performance – unnecessary data fetching.

We’re all building more complex systems, right? Our agents, whether human or AI, need more information at their fingertips than ever before. But somewhere along the line, we often fall into this trap of “just get everything, we’ll sort it out later.” Or worse, we build systems that are inherently inefficient, calling for data they don’t actually need, over and over again. It’s a habit, a bad one, and it’s costing us. Not just in terms of API calls and cloud bills, but in the actual time our agents spend waiting, and the mental load of sifting through irrelevant information.

Today, I’m not going to give you a generic overview of optimization. We’re going to dive deep into a very specific, very timely angle: Optimizing Agent Performance by Ruthlessly Eliminating Redundant Data Fetches.

The Hidden Tax: Why Redundant Data Fetches are a Bigger Deal Than You Think

Think about your agents. Are they waiting for a UI to load? Are they hitting a “refresh” button more often than they should? Are they making multiple API calls for the same piece of customer history within a single interaction? If you answered yes to any of those, you’re paying the hidden tax.

I remember working with a customer support team a few years back. Their agents were using a relatively new CRM, shiny and feature-rich. But every time an agent opened a customer’s ticket, the system would make five different API calls: one for basic customer info, one for order history, one for previous interactions, one for account status, and another for a loyalty program status. Sounds reasonable, right? Except, in about 70% of cases, the agent only needed the basic customer info and maybe the last order. The other three calls were often completely irrelevant to resolving the immediate issue.

The average load time for a customer ticket was around 4-5 seconds. Multiply that by hundreds, sometimes thousands of interactions a day, and you’re talking about hours of lost productivity. Not to mention the frustration. Agents would open tickets, see the spinner, and immediately switch to another tab to check email, breaking their focus. This wasn’t just about CPU cycles; it was about agent experience and customer experience.

This isn’t just about human agents either. If you’re running AI agents, whether they’re chatbots, recommendation engines, or automated workflow tools, every unnecessary data fetch adds latency, increases compute costs, and potentially clogs up your internal APIs. A few milliseconds here, a few kilobytes there, it all adds up. And in the world of real-time AI, those milliseconds can be the difference between a helpful response and a frustrating delay.

Spotting the Culprit: How to Diagnose Redundant Fetches

Before you can fix the problem, you need to know where it is. This is often the trickiest part, because these inefficiencies are usually baked deep into the system architecture or agent workflows.

Audit Your Agent Workflows and System Interactions

This is where you get granular. Sit with your agents. Observe them. What screens do they open? What data do they look at? What information do they actually use to make decisions or resolve issues? Don’t just rely on what they *say* they do; watch what they *actually* do.

For AI agents, analyze your system logs. What APIs are being called? What data is being returned? And critically, how much of that returned data is actually being processed or used in the subsequent steps?

Personal Anecdote: I once audited a sales agent’s process for qualifying a lead. The agent had to open the CRM, then a separate internal tool for product compatibility, then another for pricing, and finally check a shared spreadsheet for recent promotions. Each tool fetched a ton of data, much of it overlapping. We found that 80% of the time, the agent just needed to confirm the lead’s industry, company size, and previous purchase history. All the other data was only needed for the final proposal stage, not the initial qualification.

Leverage Network Monitoring and API Logging

If you have control over the applications your agents use, or if you’re building them, you have powerful tools at your disposal. Browser developer tools (Chrome DevTools, Firefox Developer Tools) are your best friends here. Look at the Network tab. Filter by XHR/Fetch requests. Watch what happens when an agent performs a common action. Are multiple requests going out for the same user ID? Are there large payloads being returned that contain fields never displayed on screen?

On the backend, your API logs should be telling a similar story. Look for repeated calls to the same endpoints within a short timeframe from the same user or agent ID. Monitor payload sizes. Are you consistently sending back 5MB JSON blobs when the UI only needs 50KB?

Example: Using Browser DevTools to Spot Redundancy

Let’s say an agent opens a customer profile page. Open your browser’s DevTools (F12 or Cmd+Option+I), go to the ‘Network’ tab, and reload the page. Look at the requests. Pay attention to:

• Request URLs: Are there multiple requests to /api/customer/{id}, /api/customer/{id}/orders, /api/customer/{id}/support-history, etc.?
• Response Sizes: How big are the responses?
• Timing: How long do these requests take?

You might find something like this:

Request URL: /api/customer/12345
Method: GET
Status: 200 OK
Size: 1.2 MB (Contains full customer details, payment methods, address history, etc.)

Request URL: /api/customer/12345/orders?limit=100
Method: GET
Status: 200 OK
Size: 850 KB (Contains 100 past orders with all line items, shipping details)

Request URL: /api/customer/12345/loyalty-status
Method: GET
Status: 200 OK
Size: 50 KB (Contains loyalty points, tier, redemption history)

If the immediate page only displays the customer’s name, email, and their last 3 orders, you’re fetching a lot of unnecessary data. The 1.2MB customer payload is likely overkill, and 100 orders when only 3 are displayed is a huge waste.

The Fix: Strategies for Eliminating Redundant Fetches

Once you’ve identified the hotspots, it’s time to get surgical.

1. Backend-Driven Data Aggregation and Transformation

Instead of the frontend making five calls, have the backend make those calls internally, aggregate the necessary data, and return a single, tailored response. This is a classic API Gateway or Backend-for-Frontend (BFF) pattern.

Before (Frontend makes multiple calls):

// Frontend code for loading customer details
async function loadCustomerDashboard(customerId) {
 const customerInfo = await fetch(`/api/customers/${customerId}`).then(res => res.json());
 const orders = await fetch(`/api/customers/${customerId}/orders?limit=3`).then(res => res.json());
 const supportTickets = await fetch(`/api/customers/${customerId}/tickets?status=open`).then(res => res.json());
 // ... render dashboard with customerInfo, orders, supportTickets
}

After (Backend aggregates):

// Frontend code for loading customer details
async function loadCustomerDashboard(customerId) {
 // Single call to a new, optimized endpoint
 const dashboardData = await fetch(`/api/agent-dashboard/${customerId}`).then(res => res.json());
 // ... render dashboard with dashboardData.customerInfo, dashboardData.orders, etc.
}

// Backend (Node.js example for /api/agent-dashboard/:customerId)
app.get('/api/agent-dashboard/:customerId', async (req, res) => {
 const customerId = req.params.customerId;
 try {
 // Make internal calls to existing microservices/databases
 const customerInfo = await customerService.getCustomer(customerId);
 const orders = await orderService.getRecentOrders(customerId, 3); // Get only 3
 const openTickets = await ticketService.getOpenTickets(customerId);

 res.json({
 customerInfo: {
 id: customerInfo.id,
 name: customerInfo.name,
 email: customerInfo.email,
 // Only send fields needed for the dashboard summary
 },
 recentOrders: orders.map(order => ({
 id: order.id,
 date: order.date,
 total: order.total,
 status: order.status
 })), // Only send fields needed for the order summary
 openTickets: openTickets.map(ticket => ({
 id: ticket.id,
 subject: ticket.subject,
 status: ticket.status
 }))
 });
 } catch (error) {
 console.error('Error fetching agent dashboard data:', error);
 res.status(500).json({ message: 'Internal server error' });
 }
});

This approach significantly reduces network overhead on the client side and allows you to tailor the payload precisely to what the agent needs for that specific view.

2. Smart Caching Mechanisms (Client-side & Server-side)

If data doesn’t change frequently, don’t fetch it every time. This sounds obvious, but you’d be surprised how often it’s overlooked.

• Client-side caching: Use browser storage (localStorage, sessionStorage, IndexedDB) for static or infrequently updated data (e.g., product catalogs, agent profiles, common FAQs). For dynamic data, consider in-memory caches within your frontend application.
• Server-side caching: Implement caching at your API layer (e.g., Redis, Memcached) for common queries. If 100 agents are all asking for the “top 10 common issues” at the start of their shift, cache that response.

Crucial point: Implement proper cache invalidation strategies. Nothing is worse than stale data. Use ETags, Last-Modified headers, or time-based expiry.

3. Granular API Endpoints and Field Selection

Instead of a single “get customer” endpoint that returns everything, create more granular endpoints or allow for field selection in your API requests.

Bad: GET /api/customer/{id} returns all customer data.

Better: GET /api/customer/{id}?fields=name,email,phone,lastOrderDate

This requires more thought in API design but gives the frontend (or AI agent) the power to request only what it needs. GraphQL is a fantastic solution for this problem, allowing clients to precisely define the data structure they require.

4. Event-Driven Updates Instead of Polling

If an agent needs to know when a customer’s status changes or a new ticket comes in, don’t have the UI constantly poll the server every few seconds. This is a massive waste of resources and generates tons of unnecessary requests.

Instead, use WebSockets, Server-Sent Events (SSE), or webhooks to push updates to the agent in real-time only when something actually changes. This dramatically reduces network traffic and ensures agents always have the most up-to-date information without constantly asking for it.

5. Lazy Loading and On-Demand Fetching

Don’t fetch data until it’s actually needed. This is particularly relevant for complex UIs with multiple tabs, accordions, or hidden sections.

• If a customer profile has a “Payment History” tab that’s rarely clicked, don’t fetch that data until the agent navigates to that tab.
• For AI agents, if a particular decision path only requires specific data, fetch it only when that path is activated.

This is where the frontend team needs to work hand-in-hand with the backend to ensure a smooth user experience without front-loading all the data.

The Payoff: Beyond Just Faster Load Times

Implementing these strategies isn’t just about shaving off a few seconds from a page load. The benefits ripple throughout your entire operation:

• Increased Agent Productivity: Less waiting means more time spent actually helping customers or completing tasks. Focus isn’t broken by spinners.
• Reduced Infrastructure Costs: Fewer API calls, smaller payloads, and less database load directly translate to lower cloud bills (compute, egress, database I/O).
• Improved System Reliability: Less stress on your APIs and databases means they are less likely to buckle under peak load.
• Better Agent Experience: A snappy, responsive system makes agents happier and reduces frustration, leading to higher morale and lower churn.
• Enhanced Customer Experience: Faster agent interactions mean quicker resolutions for customers. For AI agents, faster response times mean a more natural and helpful conversation.

I’ve seen teams go from complaining about “slowness” to praising their “blazing fast” tools just by systematically attacking redundant data fetches. It’s not glamorous work, but it’s foundational. It’s the kind of optimization that truly moves the needle for agent performance.

Actionable Takeaways

1. Audit Your Workflows (Now!): Pick one critical agent workflow (e.g., handling a common support ticket, qualifying a lead). Observe it with a stopwatch and network monitor. Document every data fetch.
2. Identify Top Offenders: Pinpoint the requests that are slow, large, or frequently repeated unnecessarily.
3. Prioritize and Plan: You can’t fix everything at once. Focus on the 20% of fetches causing 80% of the pain. Decide whether a BFF, caching, or more granular APIs are the right solution.
4. Implement Iteratively: Start with one change, measure its impact, and then move to the next. Don’t try to re-architect everything overnight.
5. Educate Your Teams: Make sure your developers, product managers, and even agents understand the cost of unnecessary data. Foster a culture of efficiency.

Don’t let your agents drown in a sea of irrelevant data. Give them only what they need, exactly when they need it. Your agents, your customers, and your budget will thank you for it.

Until next time, keep optimizing!

Jules Martin

agntmax.com

🕒 Published: April 7, 2026