🧪 Dokugent for Research¶

Use Case: Reproducible, Transparent, and Auditable AI Research

🎯 Problem¶

In research environments—especially in AI, HCI, education, and policy—it’s difficult to reproduce agent behavior, track the influence of prompts, and audit AI decisions. Jupyter notebooks and papers often lack a full trace of the model’s plan, constraints, keys, or evolution.

💡 Solution: Dokugent¶

Dokugent acts as a structured memory + protocol layer for agent-centric research.

With its certified plans, structured BYO layers, and MCP trace support, researchers can:

Track experimental setups like prompts, models, tools, and constraints.
Reproduce the exact behavior of an agent with certified snapshots.
Audit who authored what (previewer/owner signatures).
Compare different model outputs or planning strategies using simulate or compare.
Document experimental flows with embedded context (plan, criteria, conventions, byo, etc.)

🔬 Sample Research Workflows¶

1. Prompt Engineering Research¶

Store system/user prompt iterations in prompts
Track how changes affect simulated behavior via dokugent simulate --violate

2. Agent Behavior Evaluation¶

Certify plans and store memory trails
Use trace to compare how a live vs compiled agent behaves

3. HCI or Education Studies¶

Log how students, teachers, or testers interact with agents
Package sessions with byo and certified owner identities

4. Tool Performance Studies¶

Evaluate how different agents call tools under constraints
Log results via simulate, compare plans and outputs with versioned URIs

🧠 Why Dokugent for Research?¶

Version-aware memory (@timestamp)
Portable certification of plans, previews, and owners
JSON-native records usable for quantitative or qualitative studies
Built-in traceability across agent updates, prompt shifts, and tool changes
MCP-aligned for emerging multi-agent protocols and interop standards