Agentic Workflows: How AI Decides What To Do Next in 2026

Agentic workflows are AI systems where LLMs dynamically direct their own processes: deciding what to do, which tools to use, and when to change course, rather than following predetermined code paths. They trade deterministic reliability for adaptive flexibility, which means they can handle genuinely unpredictable tasks but require serious engineering for production: circuit breakers, cost controls, human-in-the-loop checkpoints, and comprehensive monitoring. Start with simpler approaches and add agent autonomy only when task variability actually demands it.

What you need to know

Traditional automation follows predetermined if-then-else rules. Your system executes the same way every time: same input, same path. It can't handle scenarios you didn't explicitly program.

Agentic workflows flip this.

The LLM decides what happens next.

You give the system a goal: "research this topic and write a report." It figures out the steps. It might search the web, read documents, call APIs, realize it needs more context, search again, synthesize results, and iterate until satisfied. The execution path emerges from the model's reasoning, not from your flowchart.

Decisions made at runtime, not design time.

Three things make this work:

Autonomous decision-making. The agent uses function calling to dynamically choose which tools to use. This maintains a critical security boundary: the model decides what to call, but your application executes the actual function.

Dynamic planning. Instead of rigid sequences, agents decompose goals and adapt execution paths based on intermediate results. When initial approaches fail, agents replan rather than abandoning the task.

Memory across iterations. Agents maintain context through short-term memory (conversation history, current task state) and long-term memory systems. They remember what they've tried, what worked, what failed.

Most implementations use ReAct: Reasoning + Acting. The agent generates a thought (what should I do?), takes an action (call a tool), observes the result, then thinks again. This interleaving of reasoning and action, grounded in actual environmental feedback, distinguishes agents from chain-of-thought prompting that can spiral into speculation.

Consider a support ticket system. Traditional workflow: route tickets by keyword matching. Agentic version: the agent reads the ticket, queries the knowledge base, checks customer history, asks clarifying questions, finds relevant documentation, drafts a response, evaluates whether it solves the problem, and revises if needed.

The agent pursues goals, not scripts.

But here's the cost.

Agents are inherently less reliable than workflows.

This isn't a bug. It's the trade-off you're accepting.

When should you use agentic workflows?

Use them when:

• Problem-solving paths genuinely vary by context
• You need dynamic tool selection across 5+ options
• Your system can tolerate initial failure rates while tuning (according to multi-agent resilience research, single agent error rates of 10% cascade to 40-60% system failure in sequential workflows)
• You've implemented circuit breakers, retry logic, and human-in-the-loop validation

Don't use them when:

• Your input space is well-defined and bounded
• You need >99% accuracy (according to enterprise development guidance, regulated industries typically require deterministic, auditable systems)
• Response time under 1 second matters

The guidance: start with hardcoded control flow and only introduce agent autonomy where task variety explicitly requires it.

The production reality:

LinkedIn's SQL Bot, Anthropic's research system (90% improvement over single agents, 15x more tokens), Capital One's agent architecture: these run in production, not demos. But they required serious engineering: hierarchical architectures, budget controls (according to enterprise AI monitoring, POCs at $5/day can hit $300,000/month without optimization), and human-in-the-loop checkpoints.

The bottom line:

Agentic workflows unlock capabilities deterministic automation can't touch. They handle the genuinely unpredictable.

But they're not magic, and they're not AGI. They're a specific architectural pattern with specific trade-offs: predictable workflows sacrifice adaptability for reliability, while autonomous agents gain flexibility at the cost of complexity and higher failure rates.

Start simple. Add complexity when the problem demands it. Budget for the engineering production requires.

Related terms

• AI Agents — Autonomous units that reason, plan, and act using LLM-powered decision-making
• LLM Orchestration — Graph-based workflow state and control flow management (e.g., LangGraph)
• Function Calling — How LLMs invoke external tools via structured JSON outputs (see function calling documentation)
• RAG — Grounding agent responses in retrieved external knowledge
• Multi-Agent Systems — Specialized agents coordinating through message passing or shared memory
• Prompt Chaining — Sequential LLM calls as a simpler alternative to full agentic workflows

Common misconceptions

"Agents are just fancy chatbots." No. Chatbots provide conversational interfaces to pre-programmed responses. Agents decompose tasks, make autonomous decisions about tool usage, maintain state across interactions, and adapt their approach based on results.

"Agentic means fully autonomous." Production systems at Capital One, Bayezian Limited (clinical trials), and others integrate human-in-the-loop as an essential architectural feature, not a workaround. HITL reduces catastrophic errors in high-stakes domains: a production-grade trade-off.

"This is basically AGI." Current agentic systems are not AGI. They're narrow AI operating within bounded problem domains. While they can dynamically select tools and pursue goals through iterative refinement, they lack the open-ended general intelligence AGI represents. That doesn't exist yet.