Unlike simple chatbots, AI agents tackle complex requests by first creating a detailed, transparent plan. The agent can even adapt this plan mid-process based on initial findings, demonstrating a more autonomous approach to problem-solving.

The significant leap in LLMs isn't just better text generation, but their ability to autonomously execute complex, sequential tasks. This 'agentic behavior' allows them to handle multi-step processes like scientific validation workflows, a capability earlier models lacked, moving them beyond single-command execution.

A well-designed AI agent can do more than automate predefined workflows. When presented with a novel, messy case with conflicting data, it can autonomously identify the most logical next step and, crucially, pinpoint the exact moment a human expert should intervene, demonstrating advanced problem-solving.

An AI agent uses an LLM with tools, giving it agency to decide its next action. In contrast, a workflow is a predefined, deterministic path where the LLM's actions are forced. Most production AI systems are actually workflows, not true agents.

Getting high-quality results from AI doesn't come from a single complex command. The key is "harness engineering"—designing structured interaction patterns between specialized agents, such as creating a workflow where an engineer agent hands off work to a separate QA agent for verification.

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.

A key breakthrough for GPT-5.5 is its stability in tasks running for over 7-8 hours, a feat previous models struggled with. This reliability is a game-changer for agentic AI, enabling complex software migrations and ambitious, long-running projects to execute autonomously without failing, fundamentally increasing the scope of work that can be delegated to AI.

OpenAI identifies agent evaluation as a key challenge. While they can currently grade an entire task's trace, the real difficulty lies in evaluating and optimizing the individual steps within a long, complex agentic workflow. This is a work-in-progress area critical for building reliable, production-grade agents.

To make AI tools like Warp more reliable, Marco Casalaina creates explicit rules (e.g., "remind me to activate owner access") and connects the agent to documentation servers. This pre-loading of context and constraints prevents common failures and improves the agent's performance on complex tasks, moving beyond simple prompting.