Session 4: Advanced Agentic Workflows

Agentic design is the architecture of thinking at scale.

We now turn to designing agentic systems for complex, multi-step research processes. This is about architectural thinking: how to give each agent a role, define quality gates, and keep the human researcher in control.

This session also develops the critical thinking to decide when such systems are appropriate, how to maintain quality control, and where human judgment remains irreplaceable. By the end of this guide, you'll move from one-off prompting to deliberate orchestration.

Learning outcomes

By the end of this session, you'll be able to:

• Design multi-agent workflows where different AI roles collaborate on complex research tasks
• Implement quality control frameworks that maintain research rigor in automated systems
• Critically evaluate when agentic approaches add value and when simpler methods work better
• Navigate the ethical and professional considerations of increasingly autonomous research systems
• Build practical, cost-effective workflows that integrate into your academic research practice

Multi-agent research architecture

           +----------------------+
           | Human architect      |
           | goal / roles / gates |
           +----------+-----------+
                      |
                delegates through
                  checkpoints
                      |
                      v
           specialized AI roles
                      |
+-----------+  +----------+  +-----------+  +----------+
| discovery |  | analysis |  | synthesis |  | critique |
+-----+-----+  +----+-----+  +-----+-----+  +----+-----+
      |             |              |             |
  relevance       logic         novelty      integration
      |             |              |             |
      +-------------+------+-------+-------------+
                             |
                       all gates pass
                             |
                             v
                  +----------+----------+
                  | validated synthesis |
                  +----------+----------+
                             |
                     feedback / refine
                             |
                             +--------> Human architect

Key concepts: the workshop prep kit

The readings below trace the arc from vision to practice: a high-level case for AI co-scientists, the engineering realities of multi-agent systems, practical implementation patterns, and how agentic workflows may reshape academic research.

• Conceptual architecture ("Towards an AI co-scientist"): the high-level vision, moving from single prompts to orchestrating a team of specialized AI roles.
• Engineering reality ("How we built our multi-agent research system"): the "what can go wrong" guide. Learning from professional teams helps anticipate and solve common problems.
• Practical implementation ("Claude Code: Best practices"): the primary lab manual, with specific instructions and code patterns for hands-on work.
• Professional context ("From Scarcity to Abundance"): how agentic workflows may change academic research practice.

Building your research architecture: a step-by-step guide

For a step-by-step implementation of an SLR with Claude Code, including screening, extraction, and synthesis, see Building an SLR with Claude Code.

Pre-class setup for Session 4

Before this session, please ensure your research environment is fully prepared:

Beyond the guide

The workflows you design here can become the foundation for dissertation research, collaborative projects, and professional research practice.

The takeaway: agentic AI does not replace human expertise. It extends what the researcher can inspect, delegate, and verify.

Go deeper

Ready to explore advanced AI capabilities?

Related Resources

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