Proof of Concept

Autonomous Operation of a test Twitter account.

Proof of Concept

Over the past few weeks, we’ve been quietly running a Twitter account entirely through an autonomous agent — powered by KIRAI.

It was an anime-tweet fan account, posting mainly frames of animes and mangas.

While the account had previously been run manually, this marked a shift to full automation — and performance slightly improved compared to manual curation.

This serves as our first real-world proof of concept:

  • LLM-driven behavior;

  • media scraping logic;

  • and automated posting.

All powered by our own agent stack.

How It Works

KIRAI runs as a headless Node.js agent, invoked by a CRON job that executes the full posting cycle once per day. No human intervention has been involved since initial setup.

Some steps in the pipeline have been changed since the making of this graphic.

1. Topic Guidance via Prompt Seeding

Even though the tweets are just images, we use a lightweight LLM prompt that helps shape the aesthetic and thematic pull of the selected images.

Claude is invoked once daily with this system prompt and no user message. It returns 3–5 image URLs for candidate selection.

2. Content Sourcing

Claude’s outputs are passed into a custom imageVerifier module that filters by:

  • Image dimensions (landscape format preferred)

  • File size

  • Duplicate detection

  • NSFW filtering (via a basic vision API integration)

If multiple candidates pass, one is selected at random.

Future improvements include a headless scraper fallback for when Claude returns dead links.

3. Twitter Media Upload

The selected image is uploaded using Twitter’s media endpoint:

We intentionally do not add captions — this keeps the account neutral, algorithm-friendly, and focused on mood.

4. Posting Schedule

An external cron service (e.g. cron-job.org) triggers the agent once per 24h. No cloud functions or persistent server is required.

The agent runs headlessly, posts once, and exits.

Results and Observations:

From February 14 to January 6, 2025, a test-subject Twitter account was run completely autonomously for 24 consecutive days. No manual interventions occurred during this window.

This is not a simulation. These are real posts, live content fed to a real audience, and an almost unsupervised agent operating in production.

Twitter Engagement

Despite having no captions, no hashtags, and no manual promotion, the bot generated impressive organic reach simply by posting visually appealing, well-curated manga content once a day.

Metric
Automated (23 days)
Manual (previous avg)

Total Tweets Posted

45

36

Total Impressions

~2,789,000

~2,300,000

Avg. Impressions per Tweet

~62,000

~53,000

Total Likes

~157,000

~123,000

Avg. Likes per Tweet

~3,489

~2,800

Top Tweet

403K views, 22K likes, 1.7K reposts

337K views, 13K likes, 1.7K reposts

New Followers Gained

+~3.4K (est.)

+~1K (est.)

Engagement Rate

~12.5–13%

~10–11%

Across nearly identical posting volumes, autonomous posts slightly outperformed manually curated ones—likely due to factors such as improved visual consistency, guidance from large language models, and reliable daily scheduling.

However, when considering the time and effort required to manually browse for content, create posts, and optimize posting schedules across different time zones, autonomous posting presents a clear efficiency advantage.

These figures represent organic reach — no paid promotion, hashtags, or algorithm gaming. The highest-performing post received 403K views and 22K likes, purely based on visual strength.

System Stability

We monitored and logged all post cycles to assess system uptime, Claude reliability, and Twitter media upload behavior.

Checkpoint
Success Rate

Prompt Execution (Claude)

98.50%

Media Fetch Success

89.00%

Media Filtering/Approval

96.25%

Twitter Upload Success

100%

Cron Execution (Daily)

100%

No critical failures were observed during the full test period.

2 posts required fallback logic due to Claude returning dead image URLs — caught by the verifier module and recovered via a cached candidate pool.

Notably, the agent maintained higher uptime consistency and a stricter daily cadence than human posting.

Log Snapshot

Each post generates an internal audit entry like:

Logs are mirrored to a private Discord webhook, allowing quick reviews and potential rollback if needed.

Performance Summary

The agent demonstrated:

  • Operational reliability under zero-maintenance conditions

  • High content relevance with only one prompt and no human filtering

  • Engagement above Twitter average for similar small accounts

  • Recoverability from LLM or image failures via custom filters

KIRAI is already capable of running niche content accounts at scale — autonomously.

Future Enhancements

Based on the success of this proof of concept, future developments may include:

  • Threaded Posts: Posting multiple images in a single thread.

  • Caption Generation: Using AI to generate contextually relevant captions.

  • User Interaction: Responding to mentions or DMs with curated content.

  • Blockchain Integration: Implementing token-based engagement rewards on Solana.

This proof of concept demonstrates the viability of using AI-driven agents for autonomous content curation and social media management. The KIRAI system showcases how combining prompt engineering, content validation, and API integration can result in a reliable and engaging social media presence without human oversight.

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