How SAP Concur automates expense reporting with agentic AI

For decades, expense automation relied on a simple premise: If the machine can read the text, it can do the work. But anyone who has ever tried to scan a crumpled, smudged, or sun-bleached receipt from their pocket knows that reading isn’t enough. When key data is missing, such as a city name or a clear date, the machine halts and the burden falls back onto the user for manual entry.

To close this gap, where traditional Optical Character Recognition (OCR) fails, SAP Concur’s engineering team set out to break new ground. While much of the industry was still focused on the design of conversational interfaces, SAP Concur foresaw a bigger shift. They recognized early on that the next leap in efficiency wouldn’t come from better scanning, but from intelligent reasoning. 

The result is an agentic AI upgrade for ExpenseIt, moving automation beyond simply reading text to solving messy logic puzzles, significantly reducing the need for manual intervention. Now, travelers can simply snap photos of their receipts as they receive them, upload digital scans, or forward receipts as emails, and ExpenseIt instantly transforms them into accurate expense entries with no date entry or itemization required. 

Bringing this next-generation system called for a partner who could push the boundaries of innovation while matching the ambition to execute at startup speeds. SAP Concur fused its visionary roadmap with Google Cloud’s full-stack AI power, partnering with the only provider that co-designs every layer, from custom silicon and data platforms to world-class models and agents. Together, the teams engineered a true breakthrough in cost management — an AI agent that not only captures the receipt but intuitively understands the business traveler’s reality.

Speed, scale, and ingenuity

Standard expense automation is great at seeing what is on receipts but can’t see what is not there. SAP Concur saw the emergence of AI agents as an opportunity to create systems that could reason, decide, and act.

Suppose you upload a lunch receipt from “The Main St. Café,” which doesn’t include the address. In the past, this missing information would completely derail the automation and require you to manually enter this data to continue.

Agentic capabilities enable analyzing contextual clues, such as a vendor’s name, expense types, and trip itinerary data, to fill in the gaps. SAP Concur wanted to create an AI agent that could think like a human assistant: “I see ‘Main St. Café.’ I also see this transaction coincides with a business trip, where the user has a flight to Dallas and a hotel in Greenville, Texas. Therefore, this vendor is probably the restaurant located near the hotel in Paris, Texas — not Paris, France.”

To solve this challenge, the teams approached the problem with a dynamic, startup-style mindset. Instead of a lengthy development cycle, the collaboration was defined by rapid prototyping and bold problem-solving. 

Utilizing Google’s Gemini models, they built the Receipt Analysis Agent, underpinned by a cognitive architecture. 

Here’s how it works:

1. Ingestion: The user snaps a photo in the SAP Concur mobile app, uploads a digital scan, or forwards a digital receipt as an email.

2. Deterministic core: SAP’s foundational technology, refined over decades of processing global expenses,  applies finely tuned logic to lift the visible text on receipts with high precision.

3. Intelligent rRouting layer: If the scanned receipt data is clear, there’s no need to trigger additional actions. If the data is ambiguous (e.g., “Missing location”), the routing logic dynamically directs the task to the Receipt Analysis Agent.

4. Contextual reasoning: Built with Gemini models, the AI agent doesn’t just guess — it uses tools and grounding to infer missing information. ExpenseIt feeds the partial receipt data to the agent, alongside grounding data like the user’s travel itinerary and business calendar.

5. ReAct (Reason and Act framework): The Receipt Analysis Agent connects the dots, validating the vendor against the location history, and then completes the expense entry.

Based on the example above, ExpenseIt identifies the receipt image as missing the location, and the intelligent routing layer triggers the Receipt Analysis Agent. Using Gemini, the agent will then identify what’s missing, analyze surrounding contextual clues and user-specific data, and make decisions based on information like travel bookings and calendar events. 

Key design patterns for successful AI agents

The Receipt Analysis Agent was designed based on the core principles from Agentic Design Patterns, a hands-on guide written by senior Google engineer Antonio Gulli. This critical guidance helped SAP Concur successfully transform ExpenseIt into a system that can reason on data both inside and outside of receipts to accurately create expense entries.

First, the teams implemented the Routing Pattern to avoid running every receipt through the AI agent, helping to optimize for both cost and intelligence. A routing architecture classifies incoming tasks: Receipts with a high OCR confidence score are routed to the standard deterministic path, while those with low scores (e.g., “Missing location) are dynamically routed to the Receipt Analysis Agent.  

Next, the Reflection Pattern is applied to solve issues like the Paris Paradox, ensuring the agent doesn’t just generate an answer like a basic chatbot. This pattern involves an internal generator-critic loop, where the model generates a hypothesis (“I think this is Paris, France”) and then acts a critic, checking it against established facts (“The itinerary says Dallas, Texas. This hypothesis is likely false.”).

Finally, the agent follows the Tool Use Pattern, providing explicit API access to grounding sources like trip itineraries from Concur Travel. This approach allows the agent to fetch the truth rather than hallucinating it, turning the system from a text generator to a factual researcher.

Architecting for ambiguity: Google Cloud’s ecosystem advantage

This project highlights a pivotal shift in intelligent system design. By combining a deterministic core with an agentic reasoning layer, SAP Concur demonstrated that AI’s highest value often isn’t in processing the data we have, but in reasoning to find the data we are missing. A defining moment in this engineering journey was the shift in how the model was utilized. The teams moved beyond treating Gemini as a generative interface and instead deployed it as a logic engine. 

Why did SAP Concur choose to build this future with Google Cloud? Because an agent is only as good as its understanding of the world — and no one understands the digital world like Google.

While this current release relies on the reasoning power of Gemini, the partnership opens the door to a future of multimodal, full-stack intelligence that’s unique in the market, including:

• Real-world grounding: Imagine an agent that cross-references a receipt with Google Maps data to ensure the business actually exists at that location.

• Frictionless flow: Future integrations could use Google Wallet to match transaction timestamps instantly, or Gmail to surface hotel folio receipts automatically.

• Edge intelligence: With mobile advancements like Gemini Nano and the service system Android AICore, sensitive processing could eventually happen right on devices, giving users speed and privacy without the data ever leaving their phone.

SAP Concur has the deep domain expertise that powers the world’s financial transactions. Google Cloud brings the full AI stack from the custom-designed chips (TPUs) optimized for training, to the mobile OS in the user’s pocket.

Ready to build your next-generation agent?

You don’t need to reinvent the wheel to build a reasoning engine like ExpenseIt. The architectural patterns discussed here — Routing, Reflection, and Tool Use — are codified directly in the Google Agent Development Kit (ADK). The ADK provides the frameworks and best practices to help you move from “prompt engineering” to “system engineering,” serving as a blueprint for building agents that are reliable, scalable, and ready for the enterprise.