Recent advances in generative AI hold the promise of complete transformation in the way we work, and the way enterprise organizations operate as a whole. In particular, the ability of AI agents to autonomously automate work on behalf of people offers tremendous opportunities for productivity and efficiency.
Every leader in every business organization faces a major challenge in this transformative moment – what’s the most effective way to harness the potential of AI agents, and avoid their pitfalls? There are two related decisions to make:
What to build vs what to buy?
Most enterprise customers decide to leverage an AI agentic automation platform (this requires a lot of deep technical knowledge and engineering to be effective) and focus their energies on building the custom agents that are unique to the business and provide the competitive advantage that maximizes value.
What AI agentic automation platform to depend on?
This is a non-trivial decision because the technology is in its early stages, it is evolving rapidly, many vendors make marketing claims to provide suitable platforms, yet the evidence is that most agentic platforms fail to meet the requirements of an enterprise business.
When applied to enterprise business scenarios, a platform for AI agentic automation has to meet an ambitious set of requirements and provide a broad set of capabilities. The design-time as well as run-time platform environment must understand human intent expressed in natural language, process human-consumable content (documents, media, messages, web pages), and interact with other legacy applications that were designed for humans. The expressive power of the application (the "logic" of the application expressed in natural language) is also important. Enterprise AI agent applications are not just question-answer systems with a search box UI or a chat UI. They typically involve complex interactions with information and actions from other business systems and applications. The application logic needs to be able to describe asynchronous long-running workflow processes, integrating with existing enterprise applications and databases. Most importantly, the platform needs to address the two primary requirements for business ROI:
The automations built on the platform must be reliable enough to put into production. This has emerged as the primary technical obstacle to agentic automation. It is very easy to build a flashy AI demo, but it has proven very difficult to produce repeatable, reliable AI agentic automation. Despite significant efforts, most vendor platforms struggle to achieve even 90% reliability on most realistic workloads.
It should not need skilled/expensive software engineers spending months or years to build and maintain an AI agentic automation. Most platforms require highly-skilled software engineers (and sometimes, ultra-expensive “forward deployed engineers”) and about a year to get close to production ready. And even then, more than 90% of projects never get good enough to deploy. AI transformation needs to rapidly happen across many departments and many scenarios in the organization, so the platform has to be able to empower and enable this scale of transformation while still ensuring security, compliance, and IT governance.
Thunk.AI is a best-of-breed application platform to implement AI agentic automation.
The platform is specialized for business applications that automate tedious human-intensive work that needs to follow a standard operating procedure (a workflow process).
Although there is a lot of optimism and marketing hype around AI agents, the reality is that two major technical problems have stood in the way of actual AI-agentic transformation among enterprise customers.
AI Reliability
Thunk.AI achieves high reliability rates for AI automation, through a combination of its unique application model, intelligent design and tuning environment, and “AI Guardian” controlled execution environment
Transformation agility
Thunk.AI has a unique no-code application model. This enables rapid iterative change which is essential to converge onto a reliable application. More importantly, it enables process owners across an organization to build, test, and deploy agentic automation without software engineer involvement.
This unique combination of development agility and AI reliability drives the rapid transformation of business operations from inefficient legacy human-intensive processes into AI-native automated workflows on Thunk.AI.