Tektonic Revives Symbolic AI for Agent-led Process Execution and Closes New Funding
Is this just RPA cloaked as AI?
Tektonic announced a new $10 million funding round yesterday, led by Madrona and Point72 Ventures. The company has managed to incorporate generative, neural, and symbolic AI alongside agents into its core messaging. You might come away thinking Tektonic has something for everyone. However, it appears that symbolic AI is the real workhorse of the solution, and maybe a simple rules engine is really at its core.
Agents Everywhere
Many startups are hesitant to talk about assistants and copilots since that places them in the crosshairs of OpenAI and Microsoft. Both companies are looking like the juggernauts of the generative AI era. Then you have Google and its large customer base. If you’d prefer to avoid direct competition with the big three, then AI agents is a good bet. If your leadership has a background in robotic process automation (RPA), agents offer even better odds of landing new funding.
Tektonic’s generative AI and neural elements are focused on intent identification and developing a fulfillment plan. Neural refers to natural language understanding (NLU) or the broader natural language processing (NLP) option when generative is not the preferred option for query intake.
The term Rules is coupled with Symbolic AI to offer up a callback to expert systems and is likely designed to insert an AI sheen on top of RPA. In some ways, it’s a more honest representation of agents than other agent vendors. Many are simply rules engines trying to pass as AI. Take a look at Rabbit, its procedural scripts, and the complete absence of anything resembling an AI large action model (LAM).
I am not saying that Tektonic is devoid of AI or is simply repackaged RPA. Nor am I suggesting RPA coupled with large language models (LLM) is somehow inferior. The coupling is logical and useful in many situations. RPA enables LLMs to execute actions and not just churn out knowledge. However, claims around AI agents have a checkered history. They have typically delivered less than advertised and been underwhelming from a technology perspective.
What is an AI Agent
An AI agent has several important characteristics, including autonomy, adaptability, and decision-making capabilities. Autonomy is the ability to execute a task on behalf of a person or system without their direct supervision. Adaptability speaks to the capability to navigate a new or changing digital environment to execute an objective without explicit instructions on how to fulfill the request. Decision-making refers to the AI agent’s ability to choose between alternatives in order to best fulfill the user or system objective.
RPA can do none of these things. It is a procedural program with imperative instructions. If the target digital services introduce changes to their APIs or user interfaces, the RPA bot typically needs to be rewritten. These bots cannot adapt and do not make decisions. Beware of RPA in AI agent clothing.
Does Tektonic Have Agents
As I mentioned in the article regarding MultiOn’s funding, AI agents are more similar to declarative instructions that state an objective and not how to fulfill it. It could be that MultiOn also uses rules engines and RPA, but it looks like the stated direction is an autonomous, adaptable decision-maker. Tektonic may get there as well. However, the way the company talks about its solution, it is clear that autonomy and delegated decision-making are not priorities.
Tektonic’s approach involves using GenAI Agents to augment employees with contextual information, guidance, and simplified actions through natural interfaces. We believe the best outcomes come from employees working interactively with the Agents, providing feedback, making decisions, and supervising. This contrasts with other approaches that focus on automating tasks using fully autonomous GenAI Agents. But when these Agents are tasked with solving complex problems and left unsupervised, they prove to be generally unreliable, prone to making poor decisions, compound mistakes, and are ultimately unable to guarantee improved outcomes.
To fulfill their promise, GenAI Agents need to work alongside humans, constrained by business rules, and paired with deterministic software. The result is an AI-augmented work environment, where human abilities are amplified, leading to better outcomes and job satisfaction.
Tektonic may be right that the risks and inconsistency of generative AI require an alternative approach. However, its approach appears to lack ambition. Add a natural language interface in front of a rules engine with some procedure-driven RPA bots to fulfill tasks and move along. This is unsurprising as the company founder is a former executive at UiPath, the market-leading RPA vendor. My expectation is that the company’s solutions will be very light on the benefits of generative AI, and its sales messages will balance between saying it is AI but the more reliable kind.
Does RPA Have a Place in the AI Era
As a final point, the intent of this discussion is not to disparage RPA. In fact, I suspect RPA will enjoy a renaissance as a helper to generative AI assistants and AI agents. When you think of function calling, you should think of an RPA bot as a function or tool that can be called to fulfill a task.
AI agents are novel in that they don’t require all the upfront detailed requirements and design to add value. However, there will be times when a quick function call that follows a step-by-step procedure will be useful and potentially optimal. RPA may also be the fastest and least expensive option in many instances. Just don’t confuse RPA and rules engines as AI, even when coupled with terms like Symbolic AI. Symbolic AI is AI, but not when its a rules engine.
Thank you to Synthedia’s sponsor: