Agent Workflows

Agentic workflows are the backbone of truly autonomous systems—those in which large language models not only respond but actively orchestrate complex, multi-step processes toward a goal. Unlike traditional workflows, where each step is rigidly predefined, agentic workflows empower models to dynamically direct their own actions, adapt mid-course, and handle deviations without external hardcoding.

From Composable Blocks to Dynamic Agency

At the core of agentic workflows lies the augmented LLM—a language model equipped with retrieval, tools, memory, and other capabilities, enabling it not only to generate text but to select and invoke services, recall previous context, and shape its own reasoning path. Such agents emerge from simple, composable components rather than monolithic frameworks. As observed by Anthropic's engineering team, "the most successful implementations weren't using complex frameworks or specialized libraries. Instead, they were building with simple, composable patterns." This insight supports our philosophy: effective systems grow from clarity, not complexity.

In practice, what starts as a modest prompt-and-call pattern evolves into a richer workflow. An augmented LLM might synthesize queries, fetch data, evaluate results, and decide its next action—all within a coherent reasoning loop. The difference between workflow and agent emerges here: workflows execute code paths, but agents control their own execution.

The Power—and Complexity—of Agentic Feedback Loops

Agentic workflows reach for ambition when multiple AI “actors” participate: orchestrators decomposing tasks, workers executing subtasks, evaluators iterating on output, all tied together in a cohesive fabric. Research highlights both opportunity and risk in deploying feedback-driven workflows. In particular, systems that rely on one model judging another can be brittle: a persuasive but mistaken critique from a single judgment step may derail reasoning, even when the initial output was correct. This kind of fragility underscores the importance of designing workflows that are robust to noise—and which treat internal feedback with care.

Building Workflows That Work in the Real World

Crucially, agentic workflows must accommodate failure, exception, and adaptation—not merely compose happy-path successions. Recent work like SHIELDA (Structured Handling of Exceptions in LLM-Driven Agentic Workflows) tackles this need head-on, providing structured exception types, classifiers, and recovery strategies that trace errors back to their reasoning roots, rather than treating them superficially. This kind of structured resilience is what elevates experimental agents into dependable systems, which is why building AI agents requires such careful consideration of failure modes and recovery strategies.

Similarly, agentic architectures in domains such as economic research have leveraged multi-agent teams, clearly defined roles, communication protocols, and human-in-the-loop checkpoints, enabling agents to autonomously drive ideation, modeling, analysis, and interpretation—while still retaining oversight and methodological integrity.

Why Agentic Workflows Are Transformational

What distinguishes agentic workflows is the shift from deterministic orchestration to dynamic agency. In traditional workflows, every step must be coded; any new branch or deviation requires developer intervention. Agentic workflows, by contrast, let models decide how to solve a problem. They enable systems to choose their methods, compose tools, and adapt their strategy dynamically—within properly defined safety rails.

This approach reshapes how we build software. Instead of scripting step-by-step logic, we define objectives, provide capable building blocks, and let the agent figure out the path. It’s a move from coded procedures to guided autonomy.

Looking Ahead

Agentic workflows are the keystone for systems that perceive, plan, and pivot without constant supervision. But building them requires balance: simplicity to preserve transparency and ease of debugging, structure to guard against error cascades, and resilience to handle the surprises that emerge when autonomy meets the real world. Successfully deploying these systems at scale also demands AI-native infrastructure that can support their long-running, stateful nature.

In the agentic future, our role shifts from commanding every step to defining the objectives, constraints, and tools—and then trusting the workflow to adapt, reflect, and deliver.