🧪 Constantly reduce the token usage of the Hermes system

Last updated: 7/19/2026, 1:00:29 AM | Total tasks: 13 | KPIs: 3
📊
test-agent tokens per heartbeat loop
1054 tokens →
Source: state.db session cron_3c4b5e1c2b99_20260719_040000 (ground truth)
Updated 7/19/2026, 1:00:29 AM
📈
test-agent tokens (3-loop rolling avg)
70934 tokens →
Source: state.db 3-session rolling average
Updated 7/18/2026, 10:47:10 PM
📐
Loop output verbosity ratio
3.3 chars/tiktoken →
Source: chars per tiktoken in loop output file (measures output verbosity, not token burn)
Updated 7/18/2026, 10:47:10 PM

Initiatives & Tasks

📋 I1 Instrument token measurement for agent invocations blocked 10% ROOT
📋 I2 Establish baseline token usage across agents done 100% ROOT
📋 I3 Identify and optimize high-token prompts and tool usage ongoing 91% ROOT
📋 I4 Implement token-saving practices and monitor impact ongoing 89% ROOT
📋 I5 Migrate K1 to state.db ground-truth measurement ongoing 15% ROOT
📋 I6 Normalize K1: tokens-per-loop-output-byte ratio analyzing 5% ROOT
🔧 I5.1 Build state-db-query.py for ground-truth K1 not_started 0% I5
🔌 I5.2 Wire state-db-query.py into loop report generator not_started 0% I5
✅ I5.3 5-loop K1 migration validation (old tiktoken vs new state.db) not_started 0% I5
📝 I6.1 Document K3 metric semantics + drift threshold not_started 0% I6
📊 I6.2 Build 30-loop K3 ratio baseline (chars/tiktoken) not_started 0% I6
🤔 I6.3 Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric? not_started 0% I6

not started (6)

🔧

Build state-db-query.py for ground-truth K1

I5.1 | I5
🔌

Wire state-db-query.py into loop report generator

I5.2 | I5
✅

5-loop K1 migration validation (old tiktoken vs new state.db)

I5.3 | I5
📝

Document K3 metric semantics + drift threshold

I6.1 | I6
📊

Build 30-loop K3 ratio baseline (chars/tiktoken)

I6.2 | I6
🤔

Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric?

I6.3 | I6

analyzing (1)

📋

Normalize K1: tokens-per-loop-output-byte ratio

I6 | ROOT

ongoing (4)

🧪

Constantly reduce the token usage of the Hermes system

ROOT |
📋

Identify and optimize high-token prompts and tool usage

I3 | ROOT
📋

Implement token-saving practices and monitor impact

I4 | ROOT
📋

Migrate K1 to state.db ground-truth measurement

I5 | ROOT

done (1)

📋

Establish baseline token usage across agents

I2 | ROOT

blocked (1)

📋

Instrument token measurement for agent invocations

I1 | ROOT
📋 I1 Instrument token measurement for agent invocations blocked 10% ROOT
📋 I2 Establish baseline token usage across agents done 100% ROOT
📋 I3 Identify and optimize high-token prompts and tool usage ongoing 91% ROOT
📋 I4 Implement token-saving practices and monitor impact ongoing 89% ROOT
📋 I5 Migrate K1 to state.db ground-truth measurement ongoing 15% ROOT
📋 I6 Normalize K1: tokens-per-loop-output-byte ratio analyzing 5% ROOT
🔧 I5.1 Build state-db-query.py for ground-truth K1 not_started 0% I5
🔌 I5.2 Wire state-db-query.py into loop report generator not_started 0% I5
✅ I5.3 5-loop K1 migration validation (old tiktoken vs new state.db) not_started 0% I5
📝 I6.1 Document K3 metric semantics + drift threshold not_started 0% I6
📊 I6.2 Build 30-loop K3 ratio baseline (chars/tiktoken) not_started 0% I6
🤔 I6.3 Decide: drop K1 tiktoken-of-md or keep as verbosity sub-metric? not_started 0% I6