The discourse around AI agents has matured. We’ve moved from “agents will replace all work” to “agents can help with specific workflows if you design them carefully.” That’s progress.

I’ve been involved in implementing agentic workflows at several organizations over the past year. Here are the patterns that seem to work.

What We Mean by Agentic Workflows

Let me be specific about terminology:

Agentic workflows involve AI systems that can make decisions, take actions, and iterate toward goals with some degree of autonomy. They’re not just chatbots answering questions - they actually do things.

The simplest version: an AI that can read an email, decide it needs information from a database, query the database, compose a response, and send it - all without human intervention for routine cases.

The complex version: multiple AI agents with different capabilities coordinating on a task, delegating subtasks to each other, and escalating to humans when needed.

Most practical deployments are closer to the simple version. The complex version makes for better demos than production systems.

Pattern 1: Tool-Augmented Single Agent

The most common successful pattern is a single agent with well-defined tools.

Architecture: One LLM-based agent with access to a set of tools (APIs, databases, actions). The agent receives a task, decides which tools to use, executes them, and produces an output.

Example: A customer service agent that can look up order status, initiate returns, update account information, and send templated communications.

Why it works:

• Simple to understand and debug
• Clear accountability (one agent, one task)
• Easier to constrain and monitor
• Tools provide deterministic actions that complement the LLM’s reasoning

Key practices:

• Define tools with clear, narrow purposes
• Implement robust input validation on tools
• Log every tool call for debugging
• Build fallbacks for tool failures

Pattern 2: Supervisor-Worker Architecture

When tasks are too complex for a single agent, a supervisor-worker pattern can help.

Architecture: A supervisor agent receives tasks, breaks them into subtasks, delegates to specialized worker agents, and synthesizes results.

Example: A research assistant where a supervisor agent receives a research question, delegates specific searches to a web search agent, data analysis to a analytics agent, and synthesis to a writing agent.

Why it can work:

• Specialization allows optimizing each worker for its task
• Supervisor provides coordination and quality control
• Easier to scale specific capabilities

Challenges:

• More complex to build and maintain
• Communication overhead between agents
• Error propagation (one worker failure can cascade)
• Harder to debug when things go wrong

Key practices:

• Clear protocols for inter-agent communication
• Supervisor should validate worker outputs
• Implement timeouts and circuit breakers
• Build observability into every interaction

Pattern 3: Human-in-the-Loop by Default

The most reliable pattern for consequential actions: assume humans should approve anything important.

Architecture: Agent operates autonomously for routine actions, but escalates to human approval for anything above defined thresholds (financial impact, customer impact, irreversibility).

Example: A procurement agent that can automatically approve small purchases but queues larger ones for human review.

Why it works:

• Captures the benefit of automation for routine work
• Maintains human oversight for important decisions
• Builds trust through demonstrated reliability
• Provides training data for expanding autonomous scope over time

Key practices:

• Clear, objective criteria for escalation
• Fast human review loops (delayed approval defeats the purpose)
• Track approval patterns to identify what can be safely automated
• Make it easy for humans to provide feedback that improves the agent

Pattern 4: Stateful Workflow Engine

For complex, multi-step processes that unfold over time.

Architecture: A workflow engine tracks state and progress. AI agents handle individual steps, but the workflow engine manages sequencing, persistence, and recovery.

Example: A loan application process where different agents handle document collection, verification, underwriting analysis, and communication - coordinated by a workflow engine that tracks each application’s status.

Why it works:

• Clean separation between intelligence (agents) and orchestration (workflow engine)
• Workflow engine handles persistence, retries, and failure recovery
• Easier to add monitoring and compliance
• Can resume from failures without losing progress

Key practices:

• Use proven workflow engine technology (Temporal, Airflow, etc.)
• Each workflow step should be idempotent
• Maintain comprehensive state history
• Build dashboards for workflow monitoring

Anti-Patterns to Avoid

From projects that struggled:

Fully autonomous multi-agent systems for business-critical processes. The complexity and unpredictability create too much risk. Every team I know that tried this ended up adding human checkpoints.

Agents with broad tool access. An agent with access to your email, calendar, file storage, and every SaaS tool you use is a security and reliability nightmare. Constrain tool access to what’s needed.

Natural language protocols between agents. Having agents communicate via natural language is flexible but unreliable. Structured protocols with schemas are more robust.

Skipping evaluation infrastructure. Building agents without ways to measure their performance leads to production systems you can’t improve systematically.

Implementation Advice

If you’re building agentic workflows:

Start narrow. Pick a workflow that’s well-defined, has clear success criteria, and isn’t mission-critical. Prove the approach before expanding.

Invest in observability. You need to see exactly what the agent is doing, why, and what tools it’s calling. When something goes wrong (and it will), you need to diagnose it.

Build robust fallbacks. What happens when the LLM is slow? When a tool fails? When the agent gets stuck in a loop? Design these scenarios into your architecture.

Version everything. Prompts, tool definitions, agent configurations - all should be versioned and reproducible. “It worked yesterday” is only useful if you can see what changed.

Plan for prompt iteration. You’ll need to refine prompts continuously. Make this easy to do and test.

Consider compliance and audit. For regulated industries, agentic workflows create documentation challenges. Every decision needs to be explainable and auditable.

The Trajectory

Agentic workflows are going to become more common. The technology works for well-scoped applications. The tooling is maturing.

But we’re not heading toward autonomous AI agents running businesses. We’re heading toward AI agents as highly capable components in human-designed workflows - handling the routine, escalating the exceptional, and continuously improving through feedback.

That’s less exciting than the hype but more useful than the skeptics suggest. And it’s happening now.