is designed as a Directed Acyclic Graph (DAG) system rather than an orchestrator for cyclic agentic architectures.

Key characteristics of Bedrock Flows:

1. It follows a DAG structure where nodes represent specific tasks/components, and edges represent the flow of information between them.

2. The "acyclic" part is crucial - it means flows must progress forward without loops or cycles. Each task can only be executed once per flow run.

3. Bedrock Flows is optimized for predictable, deterministic sequences where the path through components is known in advance.

This design differs from what's needed for true agentic architectures, which often require:

• Cyclic/iterative flows where an agent might return to previous steps

• Dynamic decision-making about which components to invoke next

• Self-reflection and adjustment of workflow based on intermediate results

If you need to implement more complex agentic patterns with Bedrock, you'd typically need to use AWS Step Functions or build custom orchestration logic to manage iterative behaviors that aren't natively supported by Bedrock Flows' DAG structure.

Lets see an example where we would use combination of Stepfunctions and bedrock flows 

Research Assistant Agent for Scientific Literature Review

Imagine an AI system that helps researchers conduct comprehensive literature reviews. The agent would need to:

1. Initial Query Processing: Parse the researcher's request for a specific scientific topic.

2. Search & Retrieval: Find relevant papers from academic databases.

3. Analysis & Synthesis: Read each paper and extract key findings.

4. Gap Identification: Identify missing information or contradictions in the current set of papers.

5. Query Refinement: Based on gaps found, reformulate search queries to find additional papers.

6. Relevance Check: Determine if the newly found papers are relevant to the original request.

7. Decision Point: Choose to either:

   • Return to step 2 (search) with refined queries

   • Continue analyzing more papers from step 3

   • Proceed to final synthesis

8. Knowledge Integration: Combine insights from all papers, highlighting agreements and disagreements.

9. Report Generation: Create a comprehensive literature review.

This requires a cyclic architecture because:

• The agent must repeatedly loop back to previous steps based on its own findings

• The path isn't predetermined but depends on what's discovered during execution

• The agent needs to make dynamic decisions about when to continue searching vs. when to finalize

• The stopping condition isn't a fixed number of iterations but based on reaching sufficient coverage

A DAG system like Bedrock Flows couldn't handle this because it doesn't support the cyclical "search → analyze → refine → search again" pattern that's essential for thorough literature review. An orchestrator that allows for conditional looping, dynamic component selection, and state tracking across iterations would be necessary.

Here's a sub-scenario from the Research Assistant Agent example that could be implemented using a DAG structure with Bedrock Flows:

Paper Analysis Pipeline (DAG Component)

This would be a well-defined subprocess within the larger cyclic architecture that handles the analysis of individual papers once they've been retrieved:

1. Document Processing: Convert the PDF/document to a text format.

2. Section Identification: Identify the abstract, introduction, methodology, results, and conclusion sections.

3. Key Finding Extraction: Extract the main findings and contributions from the paper.

4. Methodology Analysis: Identify the research methods, sample sizes, and experimental design.

5. Citation Network Analysis: Extract the paper's references and identify frequently cited works.

6. Statistical Validation: Verify statistical methods and results for soundness.

7. Data Visualization Detection: Identify charts, graphs, and tables that represent key data.

8. Limitation Recognition: Extract author-stated limitations of the research.

9. Summary Generation: Create a concise summary of the paper's contributions.

How a Combined Approach Would Work:

The cyclic orchestrator would handle the overall literature review process, including search iterations and decision-making, while delegating the analysis of each individual paper to this Bedrock Flows DAG:

1. Orchestrator (Cyclic): Decides which papers to search for and retrieve.

2. Bedrock Flows (DAG): For each paper, runs the complete analysis pipeline from document processing to summary generation.

3. Orchestrator (Cyclic): Receives the analysis results, integrates them into the growing knowledge base, identifies gaps, and decides whether to:

   • Trigger another search iteration

   • Process more papers from the current batch

   • Finalize the literature review

This combined approach leverages the strengths of both architectures:

• The predictable, step-by-step paper analysis process uses Bedrock Flows' DAG structure

• The dynamic, iterative research process that requires loops and conditional branching uses a cyclic orchestrator

This gives an example of how tasks that have well-defined, linear subcomponents can use DAGs even within a larger system that requires cyclic behaviors.

Amazon Bedrock Agents sits in an interesting position relative to both DAG-based flows and cyclic orchestrators. 

First Lets understand Agent Squads from use case and its design intent 

Use Case : Customer Support Issue Resolution

• Research Logic: Investigates customer history, previous tickets, and account details

• Technical Logic: Analyzes product logs, diagnoses technical issues, and suggests solutions

• Policy retrieve logic : Checks warranty coverage, return policies, and escalation procedures

• Communication Logic: Crafts personalized responses and determines appropriate tone

These capabilities  collaborate dynamically - the Research logic  shares findings with Technical logic , who then consults Policy retrival about coverage, while Communication logic synthesises everything into a customer response. 

Before AI World we would write these as  Traditional Microservices Architecture:

• Research Service: Returns customer data via API

• Technical Service: Processes diagnostic requests and returns status codes

• Policy Service: Validates rules and returns boolean/structured responses

• Communication Service: Formats and sends messages

Each service has predefined APIs and fixed response formats. They follow predetermined workflows.

In AI World Agent Squads are "Intelligent Microservices":

• Research Agent: Doesn't just return data - analyzes patterns, asks clarifying questions, and suggests investigation paths

• Technical Agent: Doesn't just diagnose - explains reasoning, proposes multiple solutions, and adapts based on context

• Policy Agent: Doesn't just validate - interprets edge cases, suggests alternatives, and negotiates exceptions

• Communication Agent: Doesn't just format - understands emotional context, adapts tone, and personalizes approach

Key Difference:

• Microservices: "Give me customer ID, I'll return their order history"

• Agent Squads: "Tell me about this customer issue - let me investigate their history, understand the context, and work with my team to find the best solution" ) The agents' ability to ask clarifying questions and suggest investigation paths is entirely dependent on the tools they have access to.  )

So, think of Agent Squads as "AI-powered microservices with reasoning capabilities" - they maintain service boundaries and specialization but can think, adapt, and collaborate intelligently rather than just executing predefined functions. 

This makes Agent Squads perfect for complex, dynamic problems where traditional microservices would require constant reprogramming.

Bedrock Flows and Agent Squads serve different purposes in the AWS AI ecosystem:

Bedrock Flows:

• Purpose: Visual workflow orchestration for connecting AI models, data sources, and services

• Design: Low-code/no-code drag-and-drop interface for building AI workflows

• Focus: Sequential or parallel task execution with predefined steps

• Use Case: Data processing pipelines, content generation workflows, simple automation

• Intelligence: Rule-based logic and conditional branching

• Interaction: Primarily system-to-system integration

Agent Squads:

• Purpose: Collaborative teams of autonomous AI agents working together

• Design: Agent-based architecture with role specialization

• Focus: Multi-agent collaboration with dynamic decision-making

• Use Case: Complex problem-solving requiring multiple specialized agents

• Intelligence: Autonomous reasoning, planning, and inter-agent communication

• Interaction: Agent-to-agent collaboration with human oversight

Key Differences:

Autonomy: Agent Squads have autonomous decision-making; Flows follow predefined paths.

Complexity: Agent Squads handle complex, multi-step reasoning; Flows manage structured workflows.

Collaboration: Agent Squads enable dynamic agent interaction; Flows orchestrate static service connections.

Flexibility: Agent Squads adapt to changing requirements; Flows execute predetermined sequences.

Problem-Solving: Agent Squads tackle open-ended challenges; Flows solve well-defined processes.

Think of Flows as "workflow automation" and Agent Squads as "collaborative AI teams."