If you’ve worked with agents, you’ve probably noticed that a simple chatbot won’t always cut it. Some workflows need structured decision-making, others require multiple agents working together, and sometimes, an agent isn’t even necessary at all. So, how do you decide what kind of architecture fits your use case?
Agent architectures aren’t a one-size-fits-all solution. Through working with a lot of users, we've observed distinct patterns suited to different needs.
In our previous posts, we introduced the fundamentals of agent development with A Beginner’s Guide to Building Agents in Langflow. We then explored more advanced capabilities in Building a Multi-Tool Agent in Langflow with RAG, Text2SQL, and Custom Components. Here, we’ll take a step back and focus on the bigger picture, breaking down different agent architectures and bringing them to life through simplified demos that make their design and use cases easier to understand.
When you don’t need an agent: Workflows without autonomy
Before you jump into agent design, ask yourself: do you even need one? Sometimes, a lean large-language-model-powered pipeline beats an agent hands-down, no decision-making, no fuss. These workflows grab an input, process it with fixed logic, and spit out a result. Simple, fast, done.
Example 1: Contract processor
Take a system that pulls key details from PDF contracts, parties, dates, and payment terms and outputs structured JSON. In Langflow, DataStax’s visual app and agent builder, a prebuilt OCR component extracts the text, an LLM parses it into fields, and the result lands in your database or contact management system. No agent, no overthinking, just reliable extraction every time.
Example 2: RAG for support
Picture a customer support AI. A retrieval module digs up FAQs and docs from your knowledge base, then an LLM crafts a clear, context-aware answer. No tool-switching or reasoning required—just a straight shot from query to response.
These setups reduce complexity while delivering efficient results, with no agent decision-making required. Try it in Langflow (sign up to try it free): upload a file and transform it or use Langflow’s default RAG example in your canvas. What’s the fastest workflow you can build?
Single-agent architecture: One brain, many tools
Want a straightforward AI assistant for a retail marketing analyst? A single-agent architecture is the simplest path. One agent takes charge, deciding what to do and grabbing tools like web searches, API calls, or analytics to get it done.
Example: It scrapes competitor pricing from e-commerce sites with Tavily Search, analyzes customer sentiment from X posts, and pulls Google Trends data for hot retail keywords. Ask it to “analyze holiday trends,” and it selects the right tools, generating a polished report with actionable insights, all in one go. Langflow’s visual canvas keeps it simple to build and tweak.
You can also check the last example in this article, which highlights a single agent with multiple tools.
Why it works: Ideal when one agent can own the task, no teamwork needed. But be careful—give it too many tasks, and it gets messy fast. LLMs love to try everything, but without a clear goal, you’ll get vague summaries instead of smart, precise answers.
If overloaded, a single agent can become too broad and produce shallow results. LLMs tend to generalize, so keep tasks clear and constrained. Even similar agents should have distinct roles and toolsets to prevent overlap.
Multi-agent architectures: When one agent isn’t enough
As workflows become more complex, breaking tasks into specialized agents improves efficiency and clarity. Instead of one agent doing everything, multiple agents work together, each handling a specific part of the process. Here are some common ways to structure this collaboration in Langflow.
Sequential agents: Step-by-step processing
Some workflows run best in a sequence where one agent finishes a task and hands it off tothe next.
Example: An investment research assistant automates financial analysis by structuring tasks into clear roles, each passing its work down the line.
The Researcher Agent pulls the latest data from financial news, company reports, and market trends. It scrapes stock charts, sentiment insights, and key figures using Tavily Search to ensure real-time accuracy.
The Finance Agent takes that data and runs the numbers, including stock valuation, earnings trends, and risk assessments, using Yahoo Finance and other financial tools.
The Analysis & Editor Agent transforms everything into a polished investment report with structured recommendations, charts, and comparisons investors can use.
Why it works: Perfect for complex workflows that need a clear process. Each agent stays focused, preventing overload. Just be careful, because if agents don’t pass clean, structured data, the whole chain can break down fast.
Each agent handles a specific task and passes its output to the next, keeping the workflow structured and reliable. The step-by-step process makes it easy to troubleshoot, refine, and integrate new financial data sources. This sequential setup is ideal for tasks that require validation and structured decision-making before delivering final insights.
In Langflow, this modular approach allows for easy debugging and swapping out components as needed. The trade-off? It can be slower than a single-agent system since each step relies on the previous one being completed successfully.
Hierarchical agents: Structured delegation
This architecture mirrors a management structure, where high-level agents oversee the process and delegate tasks to specialized lower-level agents.
Example: In an era where personalized outreach is key to customer engagement, automating the process without losing authenticity is a challenge. This solution uses a hierarchical agent architecture in Langflow to optimize prospecting and outreach while keeping interactions natural and effective.
Manager Agent orchestrates three specialized agents:
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A Business Research Agent that gathers company insights and key contacts,
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A Product Outreach Assistant that crafts personalized email drafts and schedules meetings
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A Voice Outreach Agent that engages prospects through dynamic, natural conversations by using Olivya.io.
By combining these agents, the system ensures a structured yet adaptable approach to outreach, making AI-driven customer engagement feel more human.
Can you find five ecommerce companies in Spain and get their email addresses and prepare one email draft for each of them?
Can you call the customer to introduce our products and try to organize a meeting next week?
Here’s the audio version of a conversation with the voice agent.
Breaking tasks into specialized roles makes it easier to scale and refine. However, too many agents can create bottlenecks. Keep multi-agent groups small and structured, ensuring each agent has a clear purpose and only the tools it needs. Define success criteria up front so each agent knows when its job is done.
A manager agent should be responsible for the outcome. This agent is not just another worker but oversees the process, assigns tasks, reviews results, and ensures the final output meets expectations.
Evaluator-optimizer: Iterative refinement
Looking for a workflow that keeps improving itself? An iterative agent loop is the way to go. One agent generates an output, another evaluates it, and an optimizer keeps refining until it meets quality standards.
Example: A resume-writing assistant. One agent drafts the resume. Another checks formatting, keyword optimization, and structure. A final agent polishes weak sections like bullet points or skills before delivering the finished version.
Why it works: Great for workflows that demand high-quality results, but without clear stopping criteria, it can spiral into endless revisions. Set boundaries to keep it focused and efficient.
Hierarchical-sequential agents: When structure meets flexibility
Let’s say you’re building an investment research assistant that delivers deep insights, stock valuations, market trends, and risk assessments—not as raw data, but as polished reports users can act on. A single agent might struggle with that workload, while a sequential chain lacks flexibility. Enter the hybrid fix: hierarchical-sequential agents in Langflow.
The base is a sequential trio that we talked about earlier in the sequential agent section. The Researcher Agent pulls financial news and market data via Tavily Search. The Finance Agent crunches valuations and risks with Yahoo Finance. Then, the Analysis & Editor Agent crafts a report with charts and recommendations; it's structured and reliable, end-to-end.
Scaling with hierarchy: Need a custom report for Microsoft (NASDAQ ticker symbol: MSFT) sent to Slack? A Manager Agent steps up. It treats the pipeline like a pro analyst, triggering it on demand, summarizing takeaways, and tailoring the output. Meanwhile, an Outreach Agent fires off a Slack update or newsletter, ensuring insights hit the right eyes, fast.
Can you prepare a report for Microsoft (MSFT) and send it to the Slack channel?
Hierarchical-sequential architectures blend structure with flexibility. The sequential pipeline handles data collection, analysis, and refinement, while the hierarchical layer manages user requests and fine-tunes outputs in real time.
With the Run Flow component, integrating these workflows is seamless. Just pick a project from the dropdown to blend structured processing with real-time adaptability. It simplifies complex projects by visually organizing multi-step workflows, making it easier to track agent interactions, debug issues, and manage dependencies without getting lost in the details.
This hybrid setup scales efficiently, making it ideal for finance, legal research, or market intelligence—any process that requires both structured analysis and adaptive decision-making.
Why it works: It keeps workflows organized while enabling flexibility. But as complexity grows, too many agents or layers can slow things down or cause context loss. Keeping information flow tight is the key to staying efficient.
Observability: The key to debugging and scaling agents
No matter the architecture, observability is essential for tracking decisions, interactions, and context flow. Langflow offers basic monitoring, while tools like LangSmith, LangWatch, and Arize provide deeper insights for debugging and optimization.
For example, if a hierarchical system produces inconsistent results, Langflow’s interface can reveal context gaps between agents, allowing for quick adjustments. Effective observability ensures maintainability, making it easier to refine and scale multi-agent systems.
One last thing: Agents aren't magic
Just one last thing to keep in mind: While agents can greatly simplify and improve your workflow, they don’t guarantee you'll get precise, identical results every time. Remember, agents depend heavily on the reasoning capabilities of LLMs and how effectively you guide them through prompting. So, keep your expectations realistic, make your prompts clear and well-structured, and don't forget to regularly evaluate and fine-tune your agents. After all, even the smartest agents need a little guidance to deliver their best work.
What’s next?
The best architecture depends on your workflow’s needs. A single-agent setup might be perfect for quick, focused tasks. A multi-agent architecture may be better for complex, multi-step processes. The key is to start simple and scale as needed.
What architectures are you experimenting with in Langflow? Share your experiences, insights, and feedback through the account menu in DataStax Langflow, or on our Discord.
