Agentic Workflows: The 4 Patterns That Actually Work

Source Talk: What's Next for AI Agentic Workflows by Andrew Ng (DeepLearning.AI) Why I picked this: Most teams are obsessed with "smarter models." Ng proves that smarter workflows are the cheaper, faster path to better results. Watch the full talk: YouTube Link

Everyone is waiting for the next massive model to solve their problems. Andrew Ng argues that's a mistake. His research shows that a weaker model (like GPT-3.5) wrapped in a robust agentic workflow often outperforms a stronger model (like GPT-4) using a zero-shot prompt.

The implication for engineering teams is massive: you don't need to wait for AGI. You need to architect better loops. Ng identifies four specific design patterns that reliably elevate model performance from "interesting demo" to "production grade."

1. Reflection (The "Look at Your Work" Loop)

Zero-shot prompting is like asking a coder to write a complex function in one go, without backspacing or testing. It rarely works.

The Pattern: Ask the model to generate code, then ask it to critique its own code, then generate a fix.

• Prompt 1: "Write code to do X."
• Prompt 2: "Check the code above for bugs and efficiency issues."
• Prompt 3: "Rewrite the code based on your critique."

Ng shows this simple loop drastically improves coding benchmarks. It forces the model to traverse the solution space iteratively rather than linearly.

2. Tool Use (The "Don't Guess" Loop)

LLMs are bad at math and facts. They are great at reasoning. The Pattern: Instead of asking the LLM to solve 349 * 232, give it a calculator tool.

• Structure: The model outputs a structured call Calculate(349, 232).
• Execution: Your system runs the code.
• Return: You feed the result back to the model.

This separates reasoning (the model's job) from computation (the tool's job). It turns the LLM from a hallucinating encyclopedia into a reasoning engine that drives reliable software.

3. Planning (The "Think Before You Act" Loop)

For complex tasks like "Research this company," a single prompt fails. The Pattern: The model generates a multi-step plan before executing step one.

• Step 1: "Break this task into sub-tasks."
• Step 2: "Execute sub-task 1."
• Step 3: "Update the plan based on the result."

This handles ambiguity. If step 1 reveals new information, the agent can rewrite the rest of the plan dynamically, rather than blindly following a rigid script.

4. Multi-Agent Collaboration (The "Roleplay" Loop)

One model playing one role has blind spots. The Pattern: Assign different "personas" to different agent instances and have them debate.

• Agent A (Coder): Writes the software.
• Agent B (Reviewer): Critiques the code for security.
• Agent C (PM): Checks if it meets requirements.

By forcing the model to switch contexts (or using separate models), you simulate a development team. The friction between roles catches errors that a single "agreeable" model would miss.

Key Insights

• Workflow > Model: A good loop beats a good model. Invest in orchestration, not just prompt engineering.
• Iterative is better than One-Shot: Human-like performance comes from the ability to revise, not the ability to get it right the first time.
• Tools are mandatory: Don't let LLMs do math or retrieval. Give them tools.

Actionable Playbook

• Audit your prompts: Are you asking for complex outputs in one shot? Break them into a "Draft -> Critique -> Refine" loop.
• Implement Tool Use: Use libraries like LangChain or the OpenAI Assistants API to give your model access to a calculator, a search engine, and your internal API.
• Start with Reflection: It's the easiest pattern to implement. Just add a "Review your work" step to your current pipeline.

Conclusion

The path to better AI isn't just "scale up." It's "loop back." By implementing these four patterns, you can build systems today that outperform the models of tomorrow.

Orchestration frameworks like LangChain and LangGraph coordinate complex workflows. These frameworks structure agent behavior—how tasks get decomposed into subtasks, how decisions cascade through the system, and how the agent recovers when errors occur.

Retrieval augmented generation connects agents to external knowledge sources. RAG pulls relevant information from databases, APIs, or document stores on demand, letting the agent reason with current data instead of relying solely on what it learned during training.

RAG: External Knowledge Integration

Retrieval augmented generation solves the knowledge staleness problem that plagues traditional models. Models trained on fixed datasets become outdated the moment training finishes. RAG sidesteps this by querying current information on demand whenever the agent needs it.

The process involves several steps working together. The agent constructs queries based on what it needs to know, retrieves relevant documents from external sources, filters the results to eliminate noise, and judges the quality of what remains. Query expansion improves accuracy—the system adds synonyms, fixes typos, and generates alternative formulations to catch information that might have been missed with the original query alone.

Filtering narrows the options down to genuinely relevant information instead of flooding the agent with everything remotely related. Quality assessment then evaluates both credibility and usefulness before incorporating any data into decisions. Not all information is created equal, and agents need to distinguish between authoritative sources and questionable ones.

What makes this architecture powerful is the separation it creates between reasoning and knowledge. The LLM handles reasoning—understanding context, making decisions, generating responses. The retrieval system handles knowledge—maintaining current information across potentially massive document collections. This separation means you can update knowledge without retraining models, and you can scale to enormous document collections without trying to embed everything into context windows.

Deployment Patterns

Four domains are seeing the most production deployment right now:

Customer service is where many organizations start. Agents handle routine queries, predict customer needs before they're explicitly stated, and route complex issues to the right specialists. The measurable benefits show up quickly—response times drop and resolution rates improve as the agents learn from successful interactions and refine their strategies for handling edge cases.

Supply chain operations benefit from agents that can predict disruptions before they cascade through the system. Route optimization happens continuously based on real-time conditions rather than static plans created days ago. Logistics coordination runs autonomously for routine decisions, eliminating the human bottleneck that used to slow everything down while still escalating genuinely complex situations.

Knowledge management agents tackle the problem every large organization faces—information trapped in dozens of systems with no good way to find what you need when you need it. These agents retrieve, organize, and surface relevant information from internal systems automatically. Employees get answers faster because the agent learns over time which sources are reliable for which types of questions and which queries need disambiguation before returning results.

Project management agents take over the tedious coordination work that bogs down human project managers. They schedule tasks, allocate resources, and track progress automatically. More importantly, they adapt when reality diverges from the plan—reprioritizing when requirements shift, adjusting schedules when delays occur, and flagging risks before they become actual blockers.

Technical Considerations

• Goal specification determines success—vague objectives produce inconsistent behavior
• Feedback loops must close fast enough to learn but not so fast they overfit to noise
• Failure modes need explicit handling—agents that can't recover gracefully create operational risk
• Monitoring tracks both task completion and decision quality over time
• Human oversight integrates at key decision points without creating bottlenecks

Business Impact & Strategy

• Reduced operational overhead from automating decision-heavy workflows
• Faster response cycles when agents handle routine decisions autonomously
• Improved adaptation to changing conditions without manual reconfiguration
• Lower coordination costs as agents manage dependencies and handoffs
• Higher consistency in decision quality across similar scenarios

Key Insights

• Agentic AI plans and executes toward goals without constant human guidance
• Reinforcement learning enables continuous improvement through operational experience
• RAG architecture keeps agents current with external knowledge sources
• Goal clarity determines whether agents deliver value or create confusion
• Feedback quality matters more than feedback quantity for learning
• Early mistakes are the cost of autonomous improvement over time

Why This Matters

The shift from reactive to agentic systems fundamentally changes operational economics in ways that matter for both cost and capability. Reactive systems scale linearly with oversight—more work means you need more humans watching and directing. Agentic systems scale autonomously—the same agent can handle growing complexity as it learns and adapts, without proportionally increasing the human oversight required.

This matters most in domains where decisions are frequent but not identical. Customer service queries vary in subtle ways that require judgment. Supply chain conditions change constantly based on weather, traffic, breakdowns, and a thousand other factors. Project requirements shift as stakeholders learn what they actually need. Human oversight simply doesn't scale to the volume and variety of decisions these domains require. Traditional automation handles repetitive identical tasks well, but these scenarios need something more flexible. Agents handle that flexibility.

The gap between pilot success and production failure determines actual ROI, and it's wider than most organizations expect. Pilots with narrow scope and controlled conditions succeed easily—they're supposed to. Production deployment with unclear goals, poor feedback loops, or inadequate monitoring fails expensively, sometimes spectacularly. The quality of your infrastructure and operational practices decides which outcome you get, not just the quality of the agent itself.

Actionable Playbook

• Define precise goals: Specify success criteria, constraints, acceptable trade-offs before deployment
• Build feedback loops: Track outcomes, connect them to decisions, close loop fast enough to learn
• Start narrow: Deploy to well-defined problem with clear success metrics; expand after proving value
• Monitor decision quality: Track not just task completion but decision rationale and adaptation patterns
• Design failure recovery: Agents will make mistakes—define how they detect and recover from errors

What Works

Define goals precisely from the start. Vague objectives inevitably produce inconsistent behavior because the agent has no clear target to optimize toward. Take the time to specify exactly what success means in measurable terms, what constraints apply in practice, and which trade-offs are acceptable when goals conflict. "Improve customer service" is too vague. "Reduce average response time below 2 minutes while maintaining satisfaction scores above 4.5" gives the agent something concrete to work toward.

Build fast feedback loops, but not so fast they create noise. Agents learn from outcomes—that's the whole point of reinforcement learning. Slow feedback delays improvement because the agent can't connect actions to consequences effectively. But feedback that's too fast risks overfitting to noise and short-term fluctuations instead of learning genuine patterns. Finding the right balance depends on your domain, but err toward faster feedback when in doubt.

Start with narrow scope and expand only after proving value. It's tempting to deploy agents broadly right away, but pilot success doesn't guarantee production success—complexity scales in non-obvious ways. Prove the agent works reliably in a well-defined problem domain before expanding to adjacent areas. Each expansion is another chance to validate assumptions and catch issues early.

Monitor continuously, and track both task completion and decision quality. This distinction matters more than you might think. Agents that complete tasks through poor decisions create technical debt and hidden problems that surface later. You want agents that solve problems well, not agents that just close tickets through shortcuts and workarounds.

Design for failure explicitly, because agents will make mistakes—especially early in deployment. Detection and recovery matter more than preventing all errors, which is impossible anyway. Build in mechanisms for the agent to recognize when it's uncertain, escalate appropriately, and recover gracefully when something goes wrong. Graceful degradation beats brittle perfection every time.

Integrate human oversight at high-leverage points, not everywhere. Requiring human approval for every decision defeats the purpose of automation. But critical decisions, edge cases the agent hasn't seen before, and genuinely novel situations should still involve humans. The art is figuring out which decisions are which, and building escalation rules that make sense for your context.

This approach works when your infrastructure actually supports autonomous operation. Clear goals, fast feedback loops, robust monitoring, graceful failure handling—these aren't optional features you add later. They're foundational requirements. Without that infrastructure foundation, agents create more operational overhead than traditional automation ever did, which defeats the entire point.