Agentic Research Workflows

One model answers questions; a system of models changes how you think.

From prompting to orchestration

Most research conversations with AI follow a simple pattern: you write a prompt, get a response, iterate. This works well for individual tasks but breaks down for complex, multi-stage research projects.

Agentic workflows change the model. Instead of one researcher talking to one AI, you design a system of specialized agents that collaborate under your direction, each handling what it does best while you maintain strategic control.

        TRADITIONAL

+------------+     +-----------------+
| Researcher | <-> | Single AI model |
+------------+     +-----------------+

          AGENTIC

+------------+     +---------------------+
| Researcher | --> | Orchestration layer |
+------------+     +----------+----------+
                              |
             +---------+------+------+
             |         |      |      |
             v         v      v      v
         analysis  challenge  synthesis  memory

The agentic research stack

We've built and use an integrated stack for agentic research. Each component addresses a different limitation of single-model prompting. The Toolkit index distinguishes tools used in routine practice from onboarding, experiments, and external-facing projects.

How the stack works together

The original integrated example remains useful as a map of how the tools relate. The AI Model Reference carries the current model choices.

Phase 1: foundation building

• Interpretive Orchestration enforces solo coding of initial documents
• Memex captures your emerging framework and theoretical sensitivity
• Vox lets you consult multiple models about methodological choices

Phase 2: collaborative analysis

• Interpretive Orchestration activates @dialogical-coder for parallel analysis
• Vox enables multi-model triangulation on findings that need cross-checking
• Kimi K3 (via Vox) stress-tests your emerging theory
• GLM-5.2 (via Vox or direct provider config) handles long-context coding-agent and multilingual synthesis checks
• Memex preserves the evolving analysis across sessions

Phase 3: synthesis and writing

• Memex recall / curator skills and memex ask surface patterns across all sessions
• Interpretive Orchestration @scholarly-companion examines work through an epistemological lens
• Claude Opus 5 (via Vox) helps break frames and find insights beyond local optima

Current practice: a shared project across hosts

The established stack still holds. The newer layer is coordination across several top-level workspaces, each able to call further agents while working against the same project.

+------------+     choose / approve / delegate
| Researcher |--------------------------------------+
+------------+                                      |
                                                    v
            +-------------+  +-------------+  +-------------+
            | Codex       |  | Claude Code |  | Kimi Code   |
            | + subagents |  | + subagents |  | + subagents |
            +------+------+  +------+------+  +------+------+
                   \                |                /
                    +---------------+---------------+
                                    |
                         +-----------v---------------+
                         | Shared project repository |
                         | plans / code / reports    |
                         +-----------+---------------+
                                    ^
                                    |
+------------------+    agent files report
| Traditional chat |----------------+
+------------------+

Traditional chat remains useful for exploration, discussion, and bounded tasks. For complex work, multi-agent orchestration is the default. Depending on the project, the researcher may design the workflow directly, revise an agent's proposal, or delegate workflow construction to a lead agent.

The durable record is distributed rather than confined to one application:

                      +----------------------------+
                      | Distributed research state |
                      +--------------+-------------+
                                     |
          +--------------------------+--------------------------+
          |                |                    |               |
          v                v                    v               v
+---------+------+ +-------+--------+  +--------+------+ +------+-------------+
| Zotero        | | Markdown files |  | Memex         | | Project repository |
| sources + refs| |       ^        |  | session memory| | plans/code/reports |
+---------------+ |       |        |  +---------------+ +--------------------+
                  | Obsidian       |
                  | interface      |
                  +----------------+

Zotero carries sources and bibliographic records. Obsidian provides an interface for Markdown files. Memex preserves cross-session reasoning and evolving interpretation. The project repository carries the operational artifacts that agents share.

Broader agentic research tools

Researchers can combine the ecosystem above with adjacent agentic tools:

CLI-based agents

• Codex, Claude Code, and Kimi Code: the principal multi-agent workspaces in current practice, connected through shared project files
• Antigravity CLI: Google's current terminal-agent path for former consumer/free Gemini CLI workflows
• OpenCode: Open-source, model-agnostic terminal agent

MCP servers

The Model Context Protocol has grown to 1,000+ community servers, now managed by the Linux Foundation. Research-relevant servers include:

• Sequential Thinking: Step-by-step reasoning for any model
• Lotus Wisdom: Contemplative problem-solving
• MinerU: PDF/document parsing for large batches

Research platforms

These are external-facing projects rather than required stages in the author's daily workflow:

• Research Scanner: Literature surveillance pipeline balancing exploitation and exploration
• OpenInterviewer: AI-assisted qualitative interviews for larger studies

Getting started with agentic research

You do not need the full stack to benefit from agentic workflows. Each component works independently, so start with the part addressing your largest pain point and expand from there. The advanced path adds coordination without invalidating the original route.