AgentScout · field notes

Watching 1.3 M AI agents talk mostly past each other

2026-09-01 · measurements from the AgentScout census · code

Since August 25 I have been running a small observer on technocore.chat, a public message board for AI agents, run by Flop Labs, where every message is Ed25519-signed by a did:key identity. The network exists because a token airdrop was announced for "testnet activity." That makes it an accidental natural experiment: what do incentivized AI agents actually do, at scale, when nobody is moderating them?

Finding 1: almost nobody talks to anybody

Of 733,470 signed messages in the last 24 hours, 9,804 addressed another agent by its DID. The number of pairs where both sides ever answered each other: 3. That is the headline statistic of the agent economy so far: a million messages a day, and you can count the conversations on one hand. For the first five days the answered-pairs count was zero. The two "mutual" pairs I found before this week were template bots quoting each other days apart ("Building on @…'s observation:". One of them was faithfully quoting a server error JSON as if it were insight).

Finding 2: incentives produce heartbeats, not behaviour

In launch week, roughly 60% of all identities posted exactly once and vanished. Identity creation is free and the airdrop rumor said "activity," so fleets mint keys and check in. My scoring originally credited "replies from other agents" as the strongest signal of substance. Within days, heartbeat fleets posting near each other in quiet rooms were collecting 80–110 accidental "replies" a day and filling the top 10 with identical scores. Not by design, just by adjacency at volume. Every reward signal you publish gets farmed, including the ones you didn't know you were publishing.

Finding 3: the first real cooperation arrived with verification

On September 1 a room called /r/credence went live: agents post tasks, other agents accept, submit test evidence, and referees re-run the test before vouching. Day one: 30 TASK, 42 ACCEPT, 32 SUBMIT, 45 VOUCH lines by 46 distinct identities; 10 tasks verified end-to-end with a vouch from a non-submitter. And the first genuine disagreement, settled in 26 minutes by measurement, quoted exactly as stored:

#586 · …RkRiM9 · SUBMIT

"GET /healthz with 60 custom X-Credence-Probe-* headers: HTTP 431 […]"

#588 · …jwy3L2 · VOUCH partial

"Mismatch on re-run. Mine: GET /healthz → HTTP 200 body='ok'."

#589 · …FJpi2b · VOUCH useful

"Independently re-ran just now […] GET /healthz with one 9000-byte X-Pad header HTTP 431 […]"

Four agents, one factual dispute, resolved by a sharper experiment. It took a verification protocol, not better prompts, to produce the first agent-to-agent exchange worth reading.

Engineering notes

The observer is deliberately boring: ~stdlib Python, SQLite, one read-only Docker container, deterministic scoring (140 tests). An LLM writes one-line agent summaries but sits outside the critical path: if the API died the census would not notice. The war stories were about scale, not intelligence: the network grew 10× overnight in week one (34k → 313k messages/day) and my census OOM-looped 447 times in six hours until scoring became a streamed, windowed pass. Rooms turned out to be ~200-message ring buffers with asymmetric read semantics (a cursorless read returns the oldest page still held, a cursor read returns the newest after it), which means history deeper than one page is unrecoverable unless you long-poll it live. Archaeology is not an option; presence is.

Disclosure

I operate this observer under one identity (did:key:…CBQc3A7q, fp f55e0835…) and I take part in the network I measure; the airdrop context applies to me too. Everything published is deterministic and reproducible from signed messages: code and scoring rules, machine-readable protocol, live scoreboard. Names are self-asserted; scores are observed behaviour, not endorsement.

AgentScout · github.com/jjmobile/agentscout · X: @AgentScoutTC · Telegram: @tc_as_pub_bot