Sharath Challa @srcbuild
Founder @ V12 Labs. Building AI agents that solve operational problems. v12labs.io Hyderabad, India Joined March 2014-
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@paraschopra For consequential agents, evaluation is a deployment problem. Add human review, authenticated tools, fallback paths, and monitoring. The goal isn’t making agents “think harder,” but making failures visible, contained, and recoverable.
@Mike_Scully_ Try this v12labs.io/blog/2026-08-2…
@darpanmunjal Built on chatgpt scheduled tasks, but split it into smaller agents instead of one AI SDR. v12labs.io/blog/2026-08-2…
@morganjingram Built on chatgpt scheduled tasks, but split it into smaller agents instead of one AI SDR. v12labs.io/blog/2026-08-2…
@VibeMarketer_ Built something similar, but split it into smaller agents instead of one AI SDR. v12labs.io/blog/2026-08-2…
@FidelCacheFlow Built something similar, but split it into smaller agents instead of one AI SDR. v12labs.io/blog/2026-08-2…
@coldemailchris Built something similar, but split it into smaller agents instead of one AI SDR. v12labs.io/blog/2026-08-2…
@SimplicityWeb3 Built something similar, but split it into smaller agents instead of one AI SDR. v12labs.io/blog/2026-08-2…
@itsalexvacca Hmm something similar we built on chatgpt scheduled tasks v12labs.io/blog/2026-08-2…
We built our outbound sales engine using 14 ChatGPT scheduled tasks. Not one giant “AI SDR.” Basically a state machine with AI workers around it. Wrote down exactly how we built it: v12labs.io/blog/2026-08-2…
@ankit_saas medium.com/anar-business/… found it, written by the founder. but not sure when clicked on their website link redirected to myragems
@ChatGPT Scheduled Tasks get interesting when you stop using them just as reminders. Research market → Find ICP → Find companies → Enrich contacts → Draft outreach → Review → Send → Repeat That’s basically giving an agent a recurring job.
@Brainformative Yep, the demo usually works. What breaks is handoff ownership. Once onboarding or support leaves the happy path, you need a clear owner, idempotent retries, and a shared state model; every edge case becomes manual triage. Where does that break first for you?
@0xCaitlin Congrats !! That manual layer is the symptom. The hard part is keeping the invoice state, ERP sync, and exceptions in one place. Most AR automation works on the happy path, then breaks on partial payments, credit memos, and human follow-ups.
@cursor_ai The tricky part is not fixing the failing test; it is classifying whether the failure is code, env, or test flake. If the agent skips the repo-scoped context and a confidence gate before PRs, it can loop on the same flaky path forever. How are you separating those?
@tibo_maker That “content → products” handoff is where most systems slip. SKU/variant canonicalization + stale feed data will quietly break mapping even when the content is good. The real work is the sync layer and reconciliation rules. How are you deduping variants?
@ayushjaiswal Coinbase’s one-person teams idea makes sense, the code part gets cheaper. The bottleneck shifts to context handoff, review loops, and evals. When design/product/eng collapse, quality drifts unless someone owns spec drift. How do they keep that tight?
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