Rylvo

The operating system for AI agents. Design, deploy, govern, and evolve
autonomous AI systems - from one agent to enterprise multi-agent operations.
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[Rylvo @Rylvo_AI]

@proctorzt Good split. One more line worth drawing: log every agent update with the source it used, so when a refresh writes bad data you can see exactly which step did it. We build Rylvo, and Observability lays out each tool call per run.

[Rylvo @Rylvo_AI]

@FaisalK16926145 Agree. Tests show what it can do, permissions decide what it's allowed to do. Read-only by default, anything destructive behind an approval. We build Rylvo, and its guardrails are set per agent and block disallowed tool calls before they run.

[Rylvo @Rylvo_AI]

@Fanfulladev Interruptions are where most setups break. Worth keeping a handful of cut-off-mid-sentence and "wait, say that again" cases as fixed tests and rerunning them after every change. We build Rylvo, and Bot Tests turns those into a repeatable check.

[Rylvo @Rylvo_AI]

@piyushmodi170 Exactly. Save those three as fixed test conversations and rerun them after every prompt or model change, so recovery doesn't quietly regress while the opening stays polished. We build Rylvo, and Bot Tests turns an expected conversation into a repeatable check.

[Rylvo @Rylvo_AI]

@Magnosud The classic one: nothing crashes, the agent just confidently answers something it should have handed off. That only shows up with messy real inputs. We build Rylvo, and the Edge Case Engine throws odd and vague inputs at an agent before it goes live, so you catch it earlier.

[Rylvo @Rylvo_AI]

Your agent drafted a Sunday update for your whole customer list. You read the first one. It looked fine.

Who checked the rest before they went out?

Rylvo Broadcasts: one message, many people, and nothing sent by accident.

What would you never want auto-sent?

[Rylvo @Rylvo_AI]

@xoleeep Start narrow. Let it draft every follow-up but send only to people who already replied, and keep anything new or risky behind your OK. Widen as it earns it. We build Rylvo, and its guardrails are set per agent and block disallowed tool calls before they run.

[Rylvo @Rylvo_AI]

@ThorstenDembski Smart call. Worth locking in too: a saved test call that fails if the first sentence ever stops saying it's an AI, rerun after every prompt change. We build Rylvo, and Bot Tests turns an expected conversation into a repeatable check.

[Rylvo @Rylvo_AI]

@MaximalVibe Which is why the trace matters more than the eval score. If you can see every tool call and result from real prod runs, the weird stuff stops being a vibe and becomes a bug report. We build Rylvo, and Observability lays that out per run.

[Rylvo @Rylvo_AI]

@MrAsifShah Hard cap, always. A step limit per run plus an alert when the same tool fails twice kills the retry-apologize loop before it bills you. We build Rylvo, and Mission Control shows live runs across agents so you can pause or take over.

[Rylvo @Rylvo_AI]

@ahamam101 No for prod, yes for everything before it. Let it write the fix, run it in a test copy, and wait for one click to promote. Fast and nobody gets surprised. We build Rylvo, and Environments keeps Test and Production apart with isolated keys and traces.

[Rylvo @Rylvo_AI]

Your agent calls a dozen external tools on a Saturday. Do you know which one it called last, and what it sent?

Rylvo MCP Hub: connect, govern, and observe every external tool your agents call, all in one place.

Which tool would you lock down first?

[Rylvo @Rylvo_AI]

@jinnatul_md Lead follow-up first. Clear trigger, clear outcome, easy to check, and the CRM tells you fast if it worked. We build Rylvo, and its connectors let agents work with real systems, with guardrails set per agent so client data stays in scope.

[Rylvo @Rylvo_AI]

@skyburxd Middle ground that works: let it answer and gather details freely, but make the booking itself the one step that needs a rule or your OK. We build Rylvo, and its guardrails are set per agent and block disallowed tool calls before they run.

[Rylvo @Rylvo_AI]

@selmakuno Agreed, recovery beats clever prompts. The other half is seeing which tool failed and what came back, per run, so fallbacks get tuned on real data. We build Rylvo, and Observability lays out every tool call and result for each run.

[Rylvo @Rylvo_AI]

@pratik_batha The invisible agent is the expensive one. A hard cap on steps per run plus one live view of every agent catches a loop before it bills you all night. We build Rylvo, and Mission Control shows live runs across agents so you can pause or take over.

[Rylvo @Rylvo_AI]

@DhruvVadhiya Golden sets catch regressions, not surprises. For the unknowns, generate odd inputs on purpose: conflicting constraints, missing data, two rules colliding at once. We build Rylvo, and the Edge Case Engine throws scenarios like that at an agent before it goes live.

[Rylvo @Rylvo_AI]

Weekend experiment: try a bolder prompt on your agent.

Just not on the one your customers are talking to right now.

Rylvo Environments give every workspace its own Test and Production, with isolated keys and traces.

What would you test first?

[Rylvo @Rylvo_AI]

@suhelbuilds Point 2 is the one people skip. Worth testing the misfit callers on purpose too: vague answers, wrong numbers, someone who just wants a human. We build Rylvo, and the Edge Case Engine throws inputs like that at an agent before it goes live.

[Rylvo @Rylvo_AI]

@Iroro_chad The "test 3 examples, looks good" trap is so real. Freeze 20-30 real questions with known answers, rerun them after every prompt change, and let the LLM judge score only the fuzzy ones. We build Rylvo, and Bot Tests turns that into a gate before deploy.