The role of agentic workflows in Intelligent Invoice Processing
Agentic workflows are defined as systems where multiple AI software agents collaborate to carry out tasks and make decisions.
User instructions to these agents are mainly submitted using natural language. The AI agents then employ Artificial Intelligence technologies, such as NLP (Natural Language Processing) and LLM (Large Language Models), to understand the instructions, execute the required tasks, and communicate the results back to the users.
This article explores the advantages and challenges of agentic workflows in the context of automated invoice processing and outlines potential future developments in this promising field.
Read more about: What is intelligent invoice processing?
Understanding agentic workflows
A typical agentic workflow consists of several agents, depending on the complexity of the workflow. These agents usually specialize in data management (extraction, processing, analysis), task management (sending emails, generating reports), decision-making (applying machine learning or rule-based logic) and communication (either among agents or with users).
Key benefits of agentic workflows
Agentic workflows offer several significant technological benefits:
- Shared enterprise knowledge: Agents can access and contribute to shared, real-time data and content repositories, facilitating seamless collaboration and enhancing their utility to users. For example, an invoice processing agent might analyze transactional invoices, unstructured contract scans, and ERP supplier master data to correlate commercial information and flag inconsistencies to users.
- Fault tolerance: If one agent becomes unavailable, another can be automatically spawned, or the workflow can adapt by employing other available resources.
- Distributed problem-solving: Agentic workflows break down complex tasks into smaller, more palatable ones, leveraging the capabilities of different agents to resolve each component effectively.
- Scalability: These workflows can scale to handle varying workloads without compromising performance, by increasing the number of available agents or by expanding their capabilities. For instance, in intelligent invoice processing, a scalable system would spawn new agents in response to rising invoice volumes caused by seasonal fluctuations, such as Black Friday in e-commerce, or outlier events like business event ticket sales. This approach optimizes costs by ensuring resources are allocated only when needed.
- Flexibility: Thanks to their modular architecture, agentic workflows can be easily reconfigured to adapt to changing policies or system environments. A change in an invoice approval policy, for example, would only require reconfiguring the specific agent enforcing that policy, reducing development, testing, and deployment time.
- Enhanced decision-making: Advanced AI systems within agentic workflows can analyze vast amounts of data to generate insights and support decision-making. They can identify patterns, trends, and anomalies that may not be immediately apparent to human analysts, thereby improving the quality of financial and strategic decisions. In intelligent invoice processing, such an agent could detect sequential transactions that aim to bypass approval limits or correlate subsequent logistical events to identify potential fraud.
Therefore, an agentic workflow, as a versatile problem-solver tool, is particularly well-suited for ad-hoc, complex business scenarios, such as real-time analyses, one-time reports or verifications, and intricate case management.
These workflows are ideal for supporting knowledge workers who need to integrate and analyze complex, disparate information from multiple systems, draw insights, make informed decisions, and execute tasks efficiently. Typical use cases include:
- Loan origination
- Fraud detection
- Risk assessment
- Support case management
- Creative endeavors
Key downsides of agentic workflows
Despite their advantages, agentic workflows are still an emerging technology and come with several significant drawbacks:
- It is still too early
While AI agents theoretically work well, the reality is proving more challenging than anticipated. For instance, the WebArena leaderboard, which benchmarks LLMs agents against real-world tasks, shows that even the best-performing models have a success rate of only 57% at best (at the time of this article). That is simply not good enough to meaningfully help knowledge workers. This will change rapidly though, as these agents are being continuously honed and adapted. In intelligent invoice processing, a success rate below 90% negates the benefits of automation.
- They are still too unreliable
It is widely recognized that LLMs are prone to hallucinations and inconsistencies, making them unreliable for tasks that require precise outputs. Currently, prompt engineering resembles more an art than a science, with the same prompt often leading to different results. This lack of reliability is particularly problematic in agentic workflows, where AI agents must decide which other agents to trigger, leading to compounded inaccuracies. Such unreliability is unacceptable in business workflows that demand exact outputs. However, this is an area expected to improve with the adoption of design patterns like function calling.
In the context of intelligent invoice processing, inaccurate invoice handling is a recipe for disaster, as invoices are typically part of a larger enterprise resource planning flow. Correcting invoice errors often involves revisiting preceding transactions, such as purchases or sales orders, or obtaining additional approvals.
- Operational performance and costs
While models like ChatGPT, Gemini, Mistral and Claude work well in basic scenarios, their use becomes cumbersome and expensive when users need to loop and retry queries. This challenge will likely diminish as technological advancements improve model efficiency and affordability.
- Legal and trust concerns
Companies may be held liable for the blunders of their AI agents. A recent example is Air Canada being ordered to pay a customer who was misled by the airline’s chatbot. Additionally, since AI is a highly non-linear technology, in many cases the way it works is akin to a “black box” – inscrutable to the human mind.
- Implementation costs and risks
Beyond the financial investment required to implement these novel architectures and integrate them into an existing infrastructure, there are significant concerns regarding internal data confidentiality, user role authorizations, and security against external threats, including prompt injections.
Due to these limitations, agentic workflows are generally not recommended for scenarios where business policies and procedures are stable, outputs need to be precise, and repetitive tasks must be carried out efficiently and cost-effectively.
Typical business use cases where straight-through processing offers a more effective solution include:
- Procure-to-Pay – these workflows rely on predefined, transparent, and auditable rules for invoice processing, verification, order management and payment approvals, ensuring consistent and efficient operations.
- Order-to-Cash – in these workflows, stable dunning policies, cash application rules, and carefully orchestrated customer communication are essential, making straight-through processing an ideal fit.
Optimal approach for Enterprise AI automation
Given the challenges of agentic workflows, a more targeted approach to AI automation in enterprise environments is recommended.
Profluo’s strategy reconciles the benefits of AI with the need for control and reliability:
- We started with a narrow scope and with simple AI
We figured out how much invoice processing can be automated with simple, straightforward, AI algorithms that are testable and auditable. This ensured a solid control point from where to jump start more complex efforts.
- We are resolving exceptions with increasingly complex AI
We are continuously analyzing invoicing deviations, exceptions, ambiguous scenarios, and mistakes, employing targeted AI algorithms to resolve these situations with increasing accuracy. This ensures robust testing and auditability before deploying complex AI in production.
- We are broadening the scope with multi-modal AI use cases
We are taking a more holistic approach towards invoice processing. What other documents should be retrieved and what information should be inferred for successful and complete invoice processing? How can AI understand and correlate them reliably with the processing task at hand? Where does the human processor spend most time and effort? Where are the biggest mistakes? Why do they occur?
- We are embracing agentic logic with pre-trained agents
We only use precise inputs and outputs in the workflow logic, understanding deviations and employing additional algorithms as needed. Rinsing and repeating.
Profluo‘s approach has led to outstanding automation and accuracy levels on significant production workloads.
Conclusion
Agentic workflows have the potential to become the go-to future of automated invoice processing if a targeted, knowledgeable approach is implemented, as opposed to a generalist, hands-off, low-code approach.
While the massive volume of authentic invoices has been instrumental in strengthening our AI invoice processing platform, it is our domain knowledge that continues to drive its expansion into new AI frontiers.
Schedule a demo today and discover the power of agentic workflows in streamlining your processes and enhancing efficiency.
