DEV Community: Satheesh Valluru

Multi-Agent Architectures: Patterns Every AI Engineer Should Know

Satheesh Valluru — Tue, 20 Jan 2026 18:19:12 +0000

Let me tell you a familiar story.
You start with a single AI agent.
It answers questions. Life is good.

Then you add:

more tools
more instructions
more edge cases
more "just one more thing" prompts

Suddenly, the agent:

hallucinates in weird ways
becomes impossible to debug
behaves differently depending on prompt order
feels more like prompt spaghetti than software

At some point, every software engineer asks the same question:

"Why doesn't this scale the way real systems do?"

The answer is simple - and uncomfortable:

You're treating an AI system like a script, not like a system.
Multi-agent architecture is the moment where AI development starts looking like software engineering again.

🧩 The Core Idea (Forget Frameworks for a Minute)

A multi-agent system is not about having "many AIs talking".

It's about:

Separation of responsibility
Clear ownership of tasks
Controlled communication
Predictable execution paths

If this sounds familiar, it should.
It's the same thinking behind:

microservices
pipelines
workflow engines
distributed systems

Agents are just workers.
Patterns are how you organize them.

🧱 Pattern 1: Sequential Pipeline

The Assembly Line

Mental model

Each agent does one thing, then hands the result to the next.

Input → Agent A → Agent B → Agent C → Output

When to use it

Each step depends on the previous one
You want deterministic, traceable behavior
The task looks like ETL, parsing, or transformation

Real-world use cases

Document processing (extract → analyze → summarize)
Code analysis (parse → lint → explain)
Data enrichment workflows

Example: Google ADK (Sequential Agent)

This pattern maps cleanly to Google ADK's sequential composition.

from google.adk.agents import LlmAgent, SequentialAgent
parser = LlmAgent(
name="Parser",
instruction="Extract raw text from the document"
)
extractor = LlmAgent(
    name="Extractor",
    instruction="Extract structured entities from text"
)
summarizer = LlmAgent(
    name="Summarizer",
    instruction="Generate a concise summary"
)
pipeline = SequentialAgent(
    name="DocumentPipeline",
    sub_agents=[parser, extractor, summarizer]
)

Common failure mode

Trying to parallelize steps that are logically dependent - this increases errors without improving speed.

🧭 Pattern 2: Router / Dispatcher

The Traffic Cop

Mental model

One agent decides who should handle the task, not how it should be solved.

User Input
   ↓
Router Agent
   ├─→ Billing Agent
   ├─→ Support Agent
   └─→ Sales Agent

When to use it

Multiple domains or specialties
Queries vary widely in intent
You want clean boundaries between expertise

Real-world use cases

Customer support systems
Enterprise copilots across departments
Multi-domain assistants (HR, IT, Finance)

Common failure mode

Letting the router also solve the problem - it should only delegate.

Example: Langchain

from typing import TypedDict
from langgraph.types import Send

class ClassificationResult(TypedDict):
    query: str
    agent: str

def classify_query(query: str) -> list[ClassificationResult]:
    """Use LLM to classify query and determine which agents to invoke."""
    # Classification logic here
    ...

def route_query(state: State):
    """Route to relevant agents based on query classification."""
    classifications = classify_query(state["query"])

    # Fan out to selected agents in parallel
    return [
        Send(c["agent"], {"query": c["query"]})
        for c in classifications
    ]

🔄 Pattern 3: Handoff

This Is No Longer My Job

Mental model

An agent starts the task, realizes it's not the best fit, and hands control to another agent.

Agent A → (handoff) → Agent B

When to use it

Tasks evolve mid-execution
One agent detects risk, complexity, or domain shift
You want graceful escalation

Real-world use cases

Research agent → domain expert agent
Chat agent → compliance or policy agent
Autonomous systems with safety checks

Common failure mode

Losing context during handoff - shared state is critical.

Example: Langchain

🧠 Pattern 4: Skill / Capability Loading

Specialists on Demand

Mental model

One agent stays in control but loads specialized capabilities only when needed.

Main Agent
   ├─ loads Legal Skill
   ├─ loads Finance Skill
   └─ loads Medical Skill

When to use it

The task is mostly linear
Domain knowledge is large but intermittent
You want to avoid prompt bloat

Real-world use cases

Legal assistants
Healthcare copilots
Knowledge-heavy enterprise tools

Common failure mode

Treating skills like permanent context - they should be temporary and scoped.

Example: Langchain

🧪 Pattern 5: Generator + Critic

Build, Then Question Yourself

Mental model

One agent generates output, another reviews, critiques, or validates it.

Generator → Critic → (accept | revise)

When to use it

High-stakes output
Quality matters more than speed
You want self-correction

Real-world use cases

Code generation + code review
Policy-sensitive text generation
Data analysis validation

Common failure mode

Infinite loops - always cap iterations.

Example - Generator/Critic Loop (LangGraph)

LangGraph excels at explicit loops.

def generate(state):
    return llm_generate(state)
def critique(state):
    return llm_review(state)
graph = {
    "generate": generate,
    "critique": critique,
    "loop": lambda s: "generate" if s.needs_revision else "end"
}

🌀 Pattern 6: Parallel Fan-Out / Gather

Divide and Conquer

Mental model

Multiple agents work independently in parallel, then results are merged.

        ┌→ Agent A ┐
Input → ├→ Agent B ├→ Merge → Output
        └→ Agent C ┘

When to use it

Tasks are independent
Latency matters
You want diverse perspectives

Real-world use cases

Market research across sources
Competitive analysis
Multi-angle summarization

Common failure mode

Parallelizing tasks that secretly depend on shared context.

Example - Parallel Agents (Google ADK)

from google.adk.agents import ParallelAgent
parallel = ParallelAgent(
    name="ResearchAgents",
    sub_agents=[market_agent, pricing_agent, news_agent]
)

🧩 Pattern 7: Custom Workflow (Graph-Based Thinking)

When Real Systems Get Real

Mental model

Agents are nodes, transitions are edges, and state is explicit.

This is where:

branching
loops
retries
fallbacks

all become first-class concepts.

When to use it

Long-running workflows
Conditional logic
Business processes with rules

Real-world use cases

Document approval systems
Data pipelines with validation gates
Autonomous decision systems

Common failure mode

Over-engineering too early - start simple, grow into graphs.

🧠 The Big Shift (This Is the Point)

The moment you adopt multi-agent patterns, you stop asking:

"What should my prompt say?"

And start asking:

"Which agent should own this responsibility?"

That's the same mental shift we made when:

we stopped writing giant classes
we stopped deploying monoliths
we introduced queues, services, and workflows

This is not an AI trend.
This is software architecture repeating itself.

🛠 Implementation Mapping (Framework-Second)

Only now - after understanding the patterns - does tooling matter.
Different frameworks simply encode these same ideas in different ways.

For example:
Some frameworks represent workflows as graphs
Others provide agent composition primitives
Some emphasize routing, others orchestration

You'll see these patterns appear clearly in tools like
LangChain (especially with graph-based orchestration) and
Google Agent Development Kit (with explicit multi-agent primitives).

But the important thing is this:

Frameworks change.
Patterns transfer.

If you understand the patterns, you can:

switch tools
evaluate new platforms
design systems that don't collapse at scale

🎯 Final Takeaway

Multi-agent systems aren't about "more AI".
They're about:

responsibility boundaries
explicit coordination
predictable execution

They're how AI systems grow up and start behaving like real software.
If you're a software engineer, this should feel familiar -
because you've been here before.

Why Data Engineers Are Becoming Agent Engineers

Satheesh Valluru — Fri, 16 Jan 2026 15:10:28 +0000

Agentic AI isn’t replacing data engineering — it’s quietly upgrading it.

Press enter or click to view image in full size

I was scrolling through LinkedIn the other day — the modern engineer’s version of reading the morning paper.

Every few posts, the same words kept popping up.

Agentic AI.
Agent workflows.
The rise of the Agent Engineer.

Somewhere between a demo video and a thread declaring “prompt engineering is dead,” a familiar, uncomfortable thought crept in:

Did I miss something?

I’ve spent years building data systems.

ETL pipelines.
Data lakes.
Streaming jobs that never truly sleep.
The kind of systems that only fail at 2 a.m., and never politely.

Now suddenly, agents were everywhere.

And the way people talked about them made it sound like this new world had very little room for data engineers.

The Story We’re Being Told About Agents
Most explanations of Agentic AI follow a familiar pattern.

You’re told agents are about:

clever prompts
powerful LLMs
letting the model “reason”
wiring a few tools together

The heroes of this story are prompt wizards and demo-first builders.
The systems magically work — until they don’t.

The unspoken implication is subtle but clear:

This is an AI-native skillset.
Traditional engineers wouldn’t really get it.

But here’s the part that experience teaches you quickly:

Agents don’t fail because prompts are weak.
They fail because systems are weak.

The Moment It Clicked
The first agent I saw fail didn’t crash spectacularly.

No stack trace.
No red alert.
No obvious error.

It just made the wrong decision.

Digging in, the reasons were painfully familiar:

It forgot earlier context
It acted on stale data
A retry triggered the same action twice
A tool call silently timed out
The language was new.
The failure modes were not.

I remember stopping mid-debug and thinking:

This isn’t an AI problem.
This is a data systems problem.

That was the moment everything clicked.

Agents, Explained Without the Hype
Once you strip away the marketing language, agents start to look surprisingly familiar — especially if you’ve built production data systems.

Agent memory is just state management
Tool calls are side-effectful operations
Multi-agent systems are distributed systems
Agent workflows are long-running DAGs with feedback loops
Frameworks like LangGraph didn’t invent these problems.

They surfaced them.

Agentic AI didn’t create a new class of engineering challenges — it brought old ones back into focus, under brighter lights.

The Unfair Advantage Data Engineers Have
To survive in production, agents need things that data engineers obsess over by default:

Clear data contracts
Schema evolution without chaos
Guardrails around retries and side effects
Observability that explains why, not just what
Cost control over long-running workflows

If you’ve ever:

debugged a pipeline where “just retry it” caused duplication
designed systems assuming everything will eventually fail
worried more about correctness than cleverness
Then you already think like an agent engineer.

Agentic AI didn’t raise the bar for engineering.
It exposed who was already operating above it.

This Isn’t a Career Reset — It’s a Shift in Leverage
Data engineering isn’t being replaced.

It’s being pulled closer to decision-making.

Instead of:

“move data from A to B”

The work increasingly becomes:

“design systems that sense, decide, and act — safely”

The fundamentals don’t change.
The surface area does.

Same skills.
More impact.
Different title.

A Question Worth Sitting With
If agents are just data pipelines that can think…

Then maybe the real question isn’t:

“Can data engineers become agent engineers?”

But:

“Who else is actually prepared to build these systems responsibly?”