u/Bartaseth

After building a multi-agent research pipeline, I realized that useful information about AI token efficiency is scattered everywhere.

There are separate tools and papers for:

- token monitoring
- prompt and semantic caching
- context compression
- model routing
- memory
- multi-agent orchestration
- pricing and benchmarks
- verification and evaluation

So we started collecting the best resources in one place.

The result is Awesome AI Tokenomics, currently with 197 verified entries across tools, research, practices, concepts, claims, and reusable setups.

It is not meant to be only another long link list. We also track maintenance and freshness, require primary sources, and keep the descriptions factual.

I would especially appreciate feedback on:

- important projects we missed
- categories that should be reorganized
- claims that need stronger evidence
- practical token-saving setups worth adding

Disclosure: I maintain this repo as part of my work at Quesma.

https://github.com/QuesmaOrg/awesome-ai-tokenomics

built a deep research pipeline around Claude Code, using Claude, Codex, Gemini, and shared memory between agents.

The first run went completely off the rails:

• 111 agents launched
• 123 claims waiting for verification
• Claude Max 5x limit gone in around 30 minutes
• no final report produced

The easy conclusion would be that subagents are bad.

I don’t think so.

Separate contexts and independent analysis are extremely useful for bigger tasks. The real problem was uncontrolled fan-out, unclear responsibilities, and using expensive models for work that cheaper models could handle.

I rebuilt the pipeline with clearer roles:

• Sonnet finds information
• Opus verifies claims
• Fable plans, orchestrates, and judges
• Codex runs and inspects tools
• Gemini gives a second opinion
• all agents share local memory

I also added stricter verification rules:

• the agent finding a claim cannot verify it
• every accepted claim needs a primary-source URL
• every source needs an exact supporting quote
• numbers must actually appear on the source page
• reject an unsupported claim, not the whole project

After these changes, the pipeline could run roughly 10x longer using subscriptions I already pay for.

My biggest takeaway is that the model itself is only one part of the system. Agent fan-out, context separation, memory, verification, caching, and orchestration can matter just as much.

How do you decide when a task deserves a separate agent and context?

I wrote a full breakdown with the architecture, scripts, failures, and lessons for Quesma, where I work:

https://quesma.com/blog/custom-deep-research-pipeline/