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Anton Manaev
@ManaevLab
17+ Years Dev. AI Architect. Engineering living systems & scaling SaaS. Founder of @UpworkAI (Smart Assistant) & @WorthIt_App. Python/FastAPI.
Bali, Indonesia
Joined May 2021
208 Following
50 Followers
405 Posts
Shopify embedded app gotcha. App Bridge needs the shopify-api-key meta tag in <head> BEFORE the Bridge script loads. Wrong order = session tokens fail silently inside the iframe, console shows nothing useful. Half a day burned on a missing string.
Built a voice agent for a paint store on Shopify + ElevenLabs. Killed the system-prompt KB. The merchant edits a row in the admin, the agent says the new thing on the next call. Smaller token bill, no redeploy, hot edits.
The real cost of durable execution isn't storage or engineering time. It's the day your checkpoint format drifts and a six-hour run resumes from v2 state into a v3 graph with a silently mismatched field. Version your state schema like an API, not like a dict.
Human-in-the-loop checkpoint question nobody answers in demos: default pause-and-ask, or default proceed-and-log? Quiet agents ship faster but eat your reputation on the 1%. Chatty agents get turned off. Per-action policy or nothing.
Agent eval pattern that changed my production bug count: three judges with different rubrics, not one averaged score. Rubric 1 grades correctness. Rubric 2 grades calibration. Rubric 3 grades cost. A single number hides which of the three you're regressing on.
Every multi-agent failure I've debugged this quarter had the same root cause: two agents were allowed to write to the same state with no arbiter. Not a model problem. Not a prompt problem. Concurrency + shared state without locks, same as any database in 2005.
@gabrielabiramia -10% tokens with better accuracy is the telling part. Manual compression trades quality for cost because humans overfit to one trace. Auto-discovery beating hand-crafted is the same lesson feature engineering learned a decade ago.
@towards_AI Good stack. The layer I'd add between Evaluation and the rest: failure mode taxonomy. Most teams skip straight from 'write prompt' to 'measure accuracy' without naming what can go wrong. Knowing the distinct failure classes for your system is what makes evals useful vs theater.
@walden_yan The honest update I've been waiting for. The setups that actually work all seem to share the same property: one main loop carries state, subagents are stateless workers with narrow scope. The second you try to make two agents equals with shared memory, coherence falls apart.
@HyperFRAME_Res The OS framing lands for me. Rental shops sell capacity, operating systems sell scheduling, isolation, and observability. For agents specifically, the missing primitive is cross-region checkpoint + resume so a run doesn't die because a region hiccuped.
@GokulSures39968 Good project for upskilling. One suggestion from running these: wire eval into the graph from day one, not at the end. The Dev agent's output needs a judge before the QA agent sees it, otherwise QA spends cycles on hallucinated code that should have been failed at gen time.
@aidenfknrich Specialist + conductor is the right decomposition. The failure mode I keep watching for: the conductor becomes the bottleneck when every handoff round-trips through it. Peer-to-peer handoff with the conductor only on escalation scales better than star topology.
@EskoBabz Architecture is the right word. 'Tell it once' assumes state, but the default is stateless + system prompt window, so every session is a new hire with amnesia. Durable memory as a first-class layer, not an afterthought on top of chat, is where this gets solved.
@avaisaziz Free tier is the wedge - NVIDIA wants the router logic running on their keys so migration cost to paid DGX Cloud drops to zero. Watching quota limits and rate caps on this one, free with no envelope is how you plan for the sunset.
@varunPbhardwaj 13 topologies is a great inventory. The gap most frameworks hide: picking topology is an 80% decision, picking the aggregation policy is the other 80%. Majority vote on debate collapses on correlated errors, weighted-by-confidence rewards the loudest agent.
@Timur_Yessenov The 29% trust number is the real headline. AI coding tools are past the adoption problem - they've hit the accountability problem. 'Intentional behavior' framing only works until a client reads their own source code in someone else's repo.
@liambryceapple Claude at the bottom while Gemini leads is the signal worth studying. Best and worst usually share calibration habits - different priors on tail risk, same prompt class. Net P&L negative across all 7 is also telling.
@Omerabdasalam @sugarjammi Workflow-based over chat-based is the right call. The other flip most teams miss: push the agent toward opt-in human checkpoints instead of opt-out. Default-quiet systems get trusted fast, default-chatty ones get muted and ignored within a week.
@Vtrivedy10 @htahir111 @addyosmani Durable execution as a primitive settles one problem and exposes another: the harness becomes the new compat layer. Checkpoint format, resume semantics, what counts as a deterministic step start drifting between runtimes fast.
The gap between agent demos and agent products hides in four places: concurrency, state durability, error recovery, cost envelopes.
Any tooling that surfaces all four from day one pays for itself the first week in prod.
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