Delegating or Doing? Understanding User Behavior in Hybrid Human-Agent Interfaces — AI agent app

Gavin Raine Dizon, Tyrone Justin Sta Maria, Jordan Aiko Deja, Yasuyuki Sumi/Delegating or Doing? Understanding User Behavior in Hybrid Human-Agent InterfacesUnknown

Large Language Models (LLMs) are increasingly embedded into applications, allowing users to complete tasks either through direct manipulation or by delegating actions to conversational agents. However, little is known about how users balance these modalities when both are available. We present a web-based content management system augmented with an LLM agent through the Model Context Protocol (MCP), enabling users to perform CRUD tasks through a graphical interface, a conversational agent, or both. We conducted a between-subjects study (N=73) comparing three interaction modes: Traditional-Only, AI-First, and Hybrid. Across sixteen scenarios, we analyzed task completion time, interaction logs, and delegation behavior. AI-assisted interaction significantly reduced clicks, page navigations, and scrolling indicating lower interaction effort. Surprisingly, these reductions did not translate into faster task completion, as task duration did not differ significantly across conditions. We also found no significant relationship between CRUD operation type and delegation, suggesting that users did not systematically avoid delegating higher-risk actions. Instead, delegation varied far more between participants than between tasks, with individual differences accounting for roughly half the variance in assistant use (ICC = .50). Our findings suggest that the primary benefit of human--agent interfaces may be reducing interaction effort rather than improving speed, and that delegation reflects who the user is more than what the task demands.

agent app

Stars0

Forks0

HF Downloads—30d

Last commit—

Refreshed5d ago

Project healthUnknownNo activity data.

Production readinessResearch / EarlyBest for exploration and prototyping.

Risk notesUnknown licenseVerify license before production use.

AgentHub Score

55 / 100

Composite score from 6 signals. How we score →

Active project

55Score

Growth

40C

Activity

30C

Documentation

70C+

Maturity

45C

Community

42C

Production

58C

GitHub stars · 8 days observed0 not enough history

snapshots

Repository activity · 8 days observedReal snapshots from pushed_at

inactivepushed

2026-08-222026-08-31

Practical assessment

Should you use it?

✓ Best for

Research and experimentation
Prototype development
Learning agentic patterns

◎ Strengths

Active community
Open source
Well-documented API

✕ Not ideal for

Untested at scale without validation
Teams without AI/ML expertise

⚠ Watch-outs

Review changelog before updating
Verify license for commercial use

Technical details

What's inside

Language—

License—

Sourcearxiv

Open source✗ No

Commercial use—

Docs—

Demo—

PaperarXiv ↗

AgentHub Score

Score 55/100

Below average

Alternatives

OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings

0 · agent app

Thinking on Shots: Consistent Multi-Shot Video Editing with Agentic Reasoning

0 · agent app

Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents

0 · agent app

Beyond Execution: Auditing Experimental Fidelity in LLM-Driven Scientific Research

0 · agent app

55 Compare all →

Recent activity

Latest commit ——

Indexed by AgentHub crawler5d ago

Monitor for new releasesongoing