Fully autonomous agents are not yet reliable for complex production use cases because accuracy collapses when chaining multiple probabilistic steps. Zapier's CEO recommends a hybrid "agentic workflow" approach: embed a single, decisive agent within an otherwise deterministic, structured workflow to ensure reliability while still leveraging LLM intelligence.
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The evolution of 'agentic AI' extends beyond content generation to automating the connective tissue of business operations. Its future value is in initiating workflows that span departments, such as kickstarting creative briefs for marketing, creating product backlogs from feedback, and generating service tickets, streamlining operational handoffs.
Contrary to the vision of free-wheeling autonomous agents, most business automation relies on strict Standard Operating Procedures (SOPs). Products like OpenAI's Agent Builder succeed by providing deterministic, node-based workflows that enforce business logic, which is more valuable than pure autonomy.
Instead of one monolithic agent, build a multi-agent system. Start with a simple classifier agent to determine user intent (e.g., sales vs. support). Then, route the request to a different, specialized agent trained for that specific task. This architecture improves accuracy, efficiency, and simplifies development.
True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.
Building features like custom commands and sub-agents can look like reliable, deterministic workflows. However, because they are built on non-deterministic LLMs, they fail unpredictably. This misleads users into trusting a fragile abstraction and ultimately results in a poor experience.
To ensure reliability in healthcare, ZocDoc doesn't give LLMs free rein. It wraps them in a hybrid system where traditional, deterministic code orchestrates the AI's tasks, sets firm boundaries, and knows when to hand off to a human, preventing the 'praying for the best' approach common with direct LLM use.
Contrary to the view that useful AI agents are a decade away, Andrew Ng asserts that agentic workflows are already solving complex business problems. He cites examples from his portfolio in tariff compliance and legal document processing that would be impossible without current agentic AI systems.
Tasklet's CEO argues that while traditional workflow automation seems safer, agentic systems that let the model plan and execute will ultimately prove more robust. They can handle unexpected errors and nuance that break rigid, pre-defined workflows, a bet on future model improvements.
Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.
When developing AI capabilities, focus on creating agents that each perform one task exceptionally well, like call analysis or objection identification. These specialized agents can then be connected in a platform like Microsoft's Copilot Studio to create powerful, automated workflows.
