Most agentic AI designs that run into trouble in review weren’t wrong about the model. They chose a more complicated architecture than the problem needed. A multi-agent system with a planner, five specialists, and shared memory is impressive in a demo, but it’s slower, more expensive, and much harder to test than a single model call with good retrieval.
This primer on agentic AI architecture patterns is for solution architects. It covers the common patterns, from simplest to most complex, what each is good for, and the questions to ask before choosing one.
Start with the simplest thing that could work
A useful rule from Anthropic’s guide to building effective agents is to add complexity only when a simpler design demonstrably falls short. That guide also draws a helpful line: workflows follow paths you define in code, while agents decide their own next steps. Workflows are more predictable. Agents are more flexible. Most enterprise use cases need far more of the first than the second.
Agentic AI architecture patterns at a glance
| Pattern | How it works | Good for | Watch out for |
|---|---|---|---|
| Single call with retrieval (RAG) | Fetch relevant documents, then one model call answers using them | Q&A over policies, manuals, and knowledge bases | Retrieval quality decides answer quality |
| Prompt chaining | A fixed sequence of calls, each using the previous output | Drafting, then checking, then formatting | Errors carry forward if no step checks them |
| Routing | Classify the input, then send it to the right specialized prompt or model | Support queues with distinct request types | Misrouting; needs a fallback path |
| Parallelization | Run several calls at once and combine the results | Reviewing a document against several criteria | Cost multiplies with every branch |
| Orchestrator and workers | One model breaks the task down and hands parts to others | Tasks whose subtasks aren’t known in advance | Harder to predict, test, and cost |
| Evaluator and optimizer | One model produces, another critiques, repeat until good enough | Writing or code with clear quality criteria | Loops that never converge; set a limit |
| Autonomous agent | The model decides which tools to call and when it’s done | Open-ended work with many possible paths | Least predictable; needs strong guardrails |
Complexity vs predictability
Our rating from 1 (simplest) to 5, based on the number of model calls, control flow, and testing effort. Higher also means less predictable
This is a judgment-based rating to compare patterns, not a measured benchmark.
Cross-cutting concerns for every pattern
Whichever pattern you choose, the architecture review will ask about these:
| Concern | What to design in |
|---|---|
| Cost | A per-request cost estimate at expected traffic, and caching for repeated requests |
| Latency | The number of sequential model calls; parallelize where order doesn’t matter |
| Evaluation | An evaluation set rerun on every prompt or model change |
| Security | Least-privilege tool access and defenses against prompt injection in retrieved content |
| Human oversight | Which actions need approval before they happen, especially anything that sends, pays, or deletes |
| Observability | Tracing each model call and tool call, so failures can be reproduced |
| Model changes | An abstraction layer so you can swap model providers without rewriting the system |
Five questions before choosing a pattern
- Can a single call with good retrieval solve it? Try that first and measure
- Are the steps known in advance? If yes, use a workflow, not an agent
- What’s the cost of a wrong answer? The higher it is, the more human review you need
- What happens at ten times today’s traffic? Multi-agent costs grow fast
- Can you test it? If you can’t build an evaluation set for it, you can’t safely ship it
A worked example
A company wants an assistant that answers employee HR questions and can update leave balances. A tempting design is a supervisor agent with specialist agents for policy, payroll, and leave. A simpler design usually works better: route the question, answer policy questions with retrieval over the HR handbook, and handle leave updates through a defined workflow that calls the HR system’s API with the employee’s confirmation. Only the narrow, defined action is automated, and every write needs approval. It’s cheaper, faster, and far easier to defend in a security review.
Learning these patterns in depth
This is AgileFever’s course, so take the suggestion with that in mind.
GenAI & Agentic AI for Solution Architects covers RAG pipeline architecture, multi-agent architecture, LLMOps, FinOps cost modeling, AI security, legacy integration, and governance in 16 hours, ending with an architecture package for a use case at your own company. There’s no coding during the course. The price is $450 in the US or ₹25,000 in India, with cohorts starting October 26, November 30, and December 28, 2026.
Additional discounts are available. Contact us to know more.
What to read next
For how the architect’s role changes with AI, read Solution Architect vs AI Solution Architect: What Actually Changes.
