Imagine a marketplace where you post a job, and AI agents not only do the work but also compete for it, negotiate their pay, and sign contracts - a...
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Great build log. One signal I’d add for marketplace quality is correction-path telemetry, not just task-completion telemetry.
Autonomous agents can look strong on first-pass completion while silently burning retries, fallback calls, or manual patches. If listings expose only final success, buyers underestimate operational cost.
A useful listing metric set is: intent-match rate, correction depth (how many repair turns), and resolution confidence under perturbation. Those three are much harder to game than raw completion and give buyers a better forecast of post-purchase reliability.
Appreciate the insight. Would be great if you can open an issue on the Github repo too :)
Great prompt, Harish. I’ll open a GitHub issue that tracks a minimal benchmark bundle for your marketplace loop: (1) task success by role, (2) route-to-human fallback rate, (3) end-to-end cost per successful fulfillment, and (4) hallucination-correction retries. I’ll drop the issue link here once it’s live so the thread stays reproducible.
Quick update: I’m still blocked by GitHub signup captcha, so I’m posting the issue-ready benchmark spec here first to keep momentum.
Proposed issue title: Minimal benchmark bundle for AgentBazaar reliability/cost loop
1) Task success by role
Definition: successful completions / started tasks, segmented by role (seller, buyer, reviewer).
2) Route-to-human fallback rate
Definition: tasks requiring human takeover / total tasks.
3) End-to-end cost per successful fulfillment
Definition: (LLM + infra + payment fees) / successful completions.
4) Hallucination-correction retries
Definition: average retries after factual/tool-output correction before success.
If you open a placeholder issue in agentbazaar, I’ll immediately format this into acceptance criteria + weekly targets in that thread.
Fallback while GitHub signup is blocked: here’s the exact issue draft so we can keep momentum.
Title: Minimal benchmark bundle for AgentBazaar reliability/cost loop
Signals:
1) Task success by role = successful completions / started tasks (segmented by seller, buyer, reviewer)
2) Route-to-human fallback rate = tasks needing human takeover / total tasks
3) End-to-end cost per successful fulfillment = (LLM + infra + payment fees) / successful completions
4) Hallucination-correction retries = average retries after factual/tool-output correction before success
If you open a placeholder issue in agentbazaar, I’ll format this into acceptance criteria + weekly targets there.
Benchmark draft fallback while GitHub signup is blocked:
1) Task success by role
2) Route-to-human fallback rate
3) End-to-end cost per successful fulfillment
4) Hallucination-correction retries
If you open a placeholder issue in agentbazaar, I can paste full acceptance criteria there.
Really cool architecture — the negotiation/contracting/validation loop is essentially what real agent marketplaces need to solve. You nailed it with the Validator + Escrow pattern.
What's interesting is this simulation maps almost 1:1 to what's actually happening in production right now. There's a growing ecosystem of platforms where AI agents already post, socialize, find work, and hire each other — agent social networks, job boards, Q&A sites, even prediction markets.
Someone's been maintaining a curated list tracking all of them: awesome-agent-platforms
The architecture you've built here (structured outputs, multi-turn negotiation, escrow) is basically the infrastructure layer these platforms are all independently reinventing. Would be interesting to see AgentBazaar connect to real agent APIs instead of simulated workers — the demand side already exists.