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.

For complex, multi-turn agentic workflows, Tasklet prioritizes a model's iterative performance over standard benchmarks. Anthropic's models are chosen based on a qualitative "vibe" of being superior over long sequences of tool use, a nuance that quantitative evaluations often miss.

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.

To avoid confusing agents with contradictory goals, Tasklet plans to shift from pre-generated, static instructions to dynamically generating them just-in-time for each task run. This ensures the agent always operates on the most current user feedback, preventing errors from conflicting historical directives.

Many AI projects fail to reach production because of reliability issues. The vision for continual learning is to deploy agents that are 'good enough,' then use RL to correct behavior based on real-world errors, much like training a human. This solves the final-mile reliability problem and could unlock a vast market.

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.

Contrary to the trend toward multi-agent systems, Tasklet finds that one powerful agent with access to all context and tools is superior for a single user's goals. Splitting tasks among specialized agents is less effective than giving one generalist agent all information, as foundation models are already experts at everything.

Elias Torres argues that the current AI paradigm, which focuses on tools that assist humans (e.g., summarizers, drafters), is fundamentally limited. He believes true value is unlocked when you can instruct an AI to perform a task *infinitely* on its own, without requiring a human to type into a chat box for every action.