Agents 2.0: From Shallow Loops to Deep Agents

From Shallow Loops to Deep Agents: Engineering AI for Long, Multi‑Step Workflows

This post explains the architectural shift from simple agent loops to *Deep Agents* that plan, delegate, and retain state.
What is the main topic?
It is about building agents that can manage long, multi‑step tasks reliably.
Why does this matter to you?

• Because shallow agents lose context and fail on complex workflows.
• Because deep agents separate planning, execution, and memory to handle prolonged tasks.
• Because engineering discipline around orchestration and storage reduces operational risk.

Shallow Agents vs Deep Agents

Shallow agents run a while loop that sends prompts to an LLM and acts on the output.
They keep their entire „state“ inside the conversation history.
Which tasks do they handle well?
Short transactional jobs with limited steps.
Which tasks do they fail at?
Complex projects that span many steps or time.
Deep Agents do something different.
They plan explicitly, they persist state externally, and they delegate to specialists.
Example prompt snippet for a shallow loop.
System: „Answer the user, then call tools as needed and return results.“
User: „Research competitors and summarize.“

Four pillars of Deep Agents

Deep agents rest on four engineering pillars.
Explicit planning so the workflow is a living plan.
Orchestration with an orchestrator delegating to sub‑agents.
Persistent memory stored in files or vector stores.
Rich, procedural instructions that provide detailed context.
Why does this help?
Because the agent updates a plan between steps and avoids endless retries.
Example plan snippet in markdown.
– TODO: collect competitor list.
– IN_PROGRESS: fetch pricing pages.
– DONE: synthesize spreadsheet.

Practical patterns and implementation

What does an orchestrator do in practice?
It assigns tasks to a Researcher, a Coder, and a Writer.
Each sub‑agent runs a focused loop with a clean context.
They write intermediate artifacts to storage and return only synthesized results.
How do you avoid context overflow?
By writing intermediate outputs to a filesystem or vector database and referencing them by path or query.
I recently tested a planner that wrote a persistent to‑do list.
The difference in focus and fewer hallucinations was noticeable.
Want a deeper read?
See a practical overview and examples at this writeup.
https://www.philschmid.de/agents-2.0-deep-agents

Wrapping up
Moving from reactive loops to proactive architecture is an engineering decision.
It requires explicit plans, hierarchical delegation, and external memory.
Why should leadership care?
Because these patterns make agentic systems reliable and auditable in production.
For teams building real automation, this is where operational value appears.