5 Reasons Dataflow Empowers Agentic AI Systems
In the traditional business world or when determining if a business is healthy or not in the investment world, cashflow is the core keyword. And as you might have heard the saying “cashflow is king”, this phrase has already implied or even highlighted how important cashflow is. This especially is true now that the world has entered a business environment with higher interest rates and it might be much higher moving forward as the economic cycle moves toward a situation characterised by inflation and stagnation.
However in the AI era, only having cashflow might not be sufficient if a business aims to continue thriving and expanding in its field, because Agentic AI systems as a core part in any businesses could not be empowered by cash alone. On the other hand, Agentic AI systems need data to perform and differentiate and enhance the performance. Furthermore, the Agentic AI system also needs a comprehensive, evolving, capable and flexible dataflow.
Thus, as cashflow is king and dataflow is the wing in the AI era, I like to share 5 reasons that a comprehensive dataflow built in the Agentic AI system can empower the system and perform better.
And in the premium section, I share a comprehensive dataflow builder playbook for people to judge if they need a more comprehensive dataflow for the Agentic AI system, what factors they need to take into consideration such as data type, data processor, data agent roles, data agent coordination workflow, database and so on for the purpose of developing an Agentic AI system with evolving, scaling and empowering performance continuously
If you want to get the complete dataflow builder playbook for the Agentic AI system development, please visit the YouTube video in the opening of the article above, and leave a comment “playbook code” in the video content comment. I will reply to you in the comment with the redemption code
We write the dataflow playbook without any AI input and AI hints, which is based on our dataflow project’s experience from different sectors. Thus, checking the playbook out is not only to explore the dataflow guideline for the Agentic AI system, but also it can be a datasource for you to train or finetune your AI model with our easy2digital human input writing which also includes our style of speaking and expression and knowledge and experiences in both business and technology
Table of Contents: 5 Reasons Dataflow Empowers Agentic AI Systems
- Purpose of Dataflow
- Context Engineering and Finetuning
- Real Time and Live Data
- Integration
- Business Database
- Brand Knowledge
- Complete Dataflow Builder Playbook
- FAQ
Purpose of Dataflow
Different with traditional dataflow in IT, marketing and engineering, a dataflow for Agentic AI systems in AI era become more critical in any businesses as it directly impact on the AI input accuracy, memory, data security and output quality and performance
In the traditional dataflow based on the perception of most people, it’s like a river from A point to B point and likely there are different checkpoints and conditions on the way to determine which branch the dataflow should go forward. Efficiency for course matters, but it more focuses on the operational efficiency, security, time and cost efficiency frontier.
However, in the AI era, the purpose of dataflow is not only just for the traditional purpose, it is getting much closer to the business performance and AI evolving capability. Take the Stock Investment Agentic AI system for example. There can be hundreds of investment metrics and signals which imply investment opportunities and risks. Agentic AI systems require investment algorithms, knowledge and investment historical results to improve the next buy or sell pricing and timing opportunity. It’s not just a traditional data feeding process, it is a kind of library and memory for the Agentic AI system to judge and make decision better time after time
Thus, any Agentic AI system must need a comprehensive dataflow to ensure the data collection works 24/7 efficiently and automatically and each input and output in AI call has been equipped with highest relevant dataset and using the right algorithms
Context Engineering and Finetuning
Context engineering and finetuning are two main strategies for improving and enhancing Agentic AI system built-in capability in terms of running tasks on top of pre-trained AI models rather than build a new language model for your business, no matter the mentioned language model is LLM, MLM or SLM
Although dataset for context engineering and finetuning might be not as dynamic and real-time as the prompt engineering, it does require a dataflow to keep context dataset cleaned, updated and upgraded continuously, for the purpose of ensuring this type of core and fundamental data sources which are used either to enhance the context and improve the output quality, or finetune the AI models for Agentic AI systems to be updated and be in the best status to perform.
A dataflow, which is capable of automatically and continuously collecting and processing raw dataset for context engineering and finetuning, needs a strategy. For example, you need to in advance understand where the datasource might come from, what type of dataset might be, what the qualified criteria can be handled as the source for context and fine tuning dataset, how to test and evaluate the performance of dataset in this field and what is the standard of keeping or discarding the dataset.
Real Time and Live Data
Different with dataset for context and finetuning, real time and live data is more dynamic and diversified and fragmented. Please don’t get me wrong. Real-time and live data can be also seen as a source for context datasource if it can meet the criteria requirements in a dataflow, but very often it is used for prompt engineering and playing a role in delivering the instant signal and trending information and up to date data that can be better to have, such as news, gossip, social hot topics and so on. It does enrich the AI output and make it look more vivid, attractive and up-to-date to audiences.
Context dataset in a dataflow is a must and prioritised as it represents a business core and reliable information which acts like an official memory data supply and rule definer. The Agentic AI system output became consistent and trusted because of the context dataset. On the other hand, real time and live data in a dataflow is one of the most important datasource to detect and identify potential perspective of dataset which is used to refresh the dataset and probably add more perspectives in the context spectrum, even though it is a data source before validation and evaluation in a dataflow
Thus, in a scientific and advance dataflow, a system which can efficiently collect, process, categorise, qualify data from real-time and live data source, no matter it’s a kind of structured dataset or unstructured one, it’s a critical section in dataflow which plays a role in supply the fresh dataset and new opportunities in an Agentic AI system
Integration
One of the main reasons why the Agentic AI system is powerful and is able to autonomously make decisions, take actions, complete tasks is because the integration capability with different platforms and AI agents can get what they need to complete tasks by calling platforms in an Agentic AI system. And actually after any calls between the platform and the system, the data stores in a state and flows to the destination based on the actual task object in a dataflow we build in an Agentic AI system
By integrating with platforms, there are two main strategies now, which is to integrate through MCPs or integrate through APIs and tool calling scripts. Basically AI agents in an Agentic AI system can detect and justify whether the task they need to complete or not, and fetch the data from integrated platforms (internal or external). In this process, AI agents detect this semantically based on given content (can be from prompt, context and so on) and call accordingly. The dataset that the agents get from the platforms is stored in the state and go to different destinations based on the tasks. The data very often can be a real set of data or a bunch of signal (conditions etc)
Thus, the underlying logic of integration whether reflects based on the business object and also integration infrastructure whether can be extensive and flexible impact the performance of Agentic AI system because it directly affects the cost of data fetching and the data processing
Here is the article regarding business considerations of agentic ai system development (including cost) , please check out if you are interested in.
Business Database
In a digital era, most businesses have their own database, CRMs, CDPs to store customer data, marketing data, operational data, finance data and so on. This section is the core one which can differentiate your Agentic AI system capability with your competitors because business dataset is unique and irreplaceable and also they are the most appropriate dataset used to grab business insight and optimise the Agentic AI system output quality
As mentioned earlier in the integration capability, business databases can integrate with Agentic AI systems through MCP and APIs with tool call functions. However, the most critical consideration should be not only just the integration, but also what business data can be used or what data should not be used. What dataset can be used but can not be shown to the customer. No wonder that you do not want the system showing or leaking some business confidential data or sensitive data to the public. In particular, some sectors such as banking, credit card and so on might have some strict data compliance. Compliance violations can cause severe business problems or even the license can be suspended. Therefore, handling business database integration is not easy because it needs a more detailed and thoughtful plan to avoid problems caused by data leak from AI output
In a dataflow, a validator and checker is a must to make sure the data fed to the agents in the dataflow is usable, safe and no-risk to the business
Brand Knowledge
Lastly, any AI models deployed and integrated in the Agentic AI system basically do not know anything about your brand. Generative engine optimization requires brands to submit business information, so the Agentic AI system needs you to train it up as well.
Compared to context engineering, brand knowledge is more focused on the brand introduction, product value propositions, channel availability in the market, terms and conditions, rules such as data private, refund and so on. And in an Agentic AI system, there might be more than one AI agent and each AI agent might represent different division of a business so that the brand guideline FAQ should be varied normally