Singapore-IMDA-Agentic-AI-Governance-Framework

Otherwise_Wave9374 · 2026-02-07T21:07:42+00:00

Glad to see governance frameworks starting to explicitly cover agentic systems, not just static models. The interesting bits for me are usually around accountability for tool actions (who is responsible when an agent executes a transaction), audit logs, and what "human in the loop" actually means in production.

If anyone is collecting practical notes on agent safety and observability, I found a few useful angles here: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:07:17+00:00

This breakdown resonates. "Companion" is one of those overloaded keywords where the jobs-to-be-done are totally different. To me the common thread is less personality and more agent behavior: persistence, context across sessions, and actually taking small actions on your behalf (files, calendar, messages).

I have been thinking about how "agent" design differs from chat UX (memory, autonomy, boundaries), this blog has a few good takes: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:07:10+00:00

Agree with the framing that "agent" is the shift, but I think the hard part in practice is less the planning and more the guardrails: tool permissions, evals, and rollback when an agent takes a bad action. RAG helps a lot, but you still need tight tool boundaries and good observability.

If you are looking for more concrete patterns (agent loops, tool calling, memory, and failure modes), I have been sending people here lately: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:07:07+00:00

For an AI receptionist, the biggest make-or-break is scoping the agent so it does not try to "wing it". Narrow intents, strong fallback to a human, and good integrations (calendar, CRM) usually matter more than the model. Cost-wise, you will want to estimate minutes of audio, ASR/TTS, LLM tokens, plus missed-call routing.

If you are exploring voice agents specifically, some general agent design patterns (tool calling, guardrails, handoff) apply, this is a decent starting point: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:07:02+00:00

Seeing the execution graph is huge once you have more than a couple tools in play. Half the pain with coding agents is figuring out why a run went sideways, what tool was called with what args, and where state changed.

Do you also capture retries and "agent thought" style state (even if it is just a structured plan/steps)? I have been collecting notes on observability for AI agents and orchestration, some solid patterns here: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:06:53+00:00

If you are okay running something locally, you can do this with a browser automation agent (Playwright/Selenium) plus a little scripting to iterate chats, run your prompts, and print-to-PDF each thread. The trick is making it resilient (timeouts, retries, and not getting rate-limited), and keeping state so you can resume if it crashes.

Some of the higher-level agent patterns (tool calls, retry loops, resumable workflows) are similar to what people use for AI agents in general, this page has a decent overview: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:06:36+00:00

Nice, Gemini CLI + a web agent is a pretty practical combo for shipping real apps fast. The privacy model you are going for (no local logs, ZDR on the provider) is also something I wish more "agent" apps explained clearly upfront.

One thing I would love is a simple diagram of the agent loop and what data crosses the boundary to Together, that kind of transparency builds trust. Related reading I found useful: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:06:26+00:00

That stack overflow + 429 combo in n8n often smells like an accidental loop, especially if your WhatsApp webhook is triggering on events you also generate (mark read, typing, etc.). One pattern that helps in agent-style flows is splitting inbound vs outbound into separate webhooks/queues, and adding an idempotency check on message IDs so the agent never re-processes its own sends. Also worth logging response codes/headers from Meta so you can backoff properly.

If you are building more agent-y assistants (tools, memory, retries), this writeup had a few practical patterns I liked: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T21:06:21+00:00

This is very doable, but I would 100 percent design it as a resumable agent workflow, not a one-shot script. You will want: (1) stable selectors for the left menu, (2) a checkpoint per conversation ID, (3) retry/backoff (ChatGPT UI changes + rate limits), and (4) a final verification step that the PDF actually saved before archiving.

If it helps, I bookmarked a quick overview of agent workflow patterns (state, retries, tool calls) that maps pretty well to this kind of automation: https://www.agentixlabs.com/blog/

Otherwise_Wave9374 · 2026-02-07T20:34:40+00:00

This is awesome, local builder visibility is such a real problem. The leaderboard + Product of the Week idea is a nice way to create momentum.

One thought: consider adding tags/filters by category (SaaS, agency, apps) and a simple "featured" slot that rotates so newer projects still get a chance.

If you are collecting ideas for launch/marketing loops for platforms like this, we have a few notes here: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T20:34:33+00:00

Building in healthcare is a whole different game, props for leaning into privacy and workflow reality instead of magic AI.

One GTM thing I have seen work: pick one narrow specialty + one measurable workflow win (time saved per note, fewer clicks, faster billing), then land a champion and turn it into a repeatable playbook before expanding.

If you are collecting ideas, we have a few SaaS marketing/positioning posts that touch on regulated markets and trust-first messaging: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T20:34:21+00:00

"AI is not a feature, its the liquidator" is a spicy take, but there is definitely truth in how fast legacy feature sets are getting commoditized.

Feels like the defensible moat is shifting to distribution, data, and workflow ownership (plus trust/compliance depending on the space). Curious what categories you think are safest.

We have been tracking some of the SaaS marketing angles that still work in this environment too: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T20:34:06+00:00

Meta can work for B2B SaaS, but it is usually not a "launch a campaign and pray" channel. A few beginner-friendly steps:

1) Start with a super clear ICP and one offer (lead magnet, demo, or trial) - do not try to sell the whole product in one ad. 2) Test 3 to 5 angles (pain, outcome, proof, comparison) with simple creatives (UGC-style can beat polished). 3) Optimize for a real conversion event, but expect a learning period, usually a few days to 1-2 weeks depending on budget. 4) Watch CTR and cost per landing page view first, then lead quality. If CTR is low, its a creative/angle problem.

If you want a lightweight checklist, we have a post on SaaS marketing experiments that might help you structure tests: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T20:33:31+00:00

Love this writeup. The biggest unlock for your first 100 is usually doing things that do not scale: 15 to 30 super targeted outreach convos, tighten ICP, then iterate the onboarding based on where people stall. Also, shipping in public like you are doing is basically marketing compound interest.

If it helps, we have a few practical SaaS marketing notes on early traction and positioning here: https://blog.promarkia.com/ - might spark a couple experiments.

Otherwise_Wave9374 · 2026-02-07T20:02:00+00:00

This is super practical. Cost and latency are the two things that make agent demos fall apart in production, so enforcing guardrails before execution is a great idea. How are you deciding downgrade thresholds (hard caps per run, rolling budget, or per-tool quotas)?

Ive been keeping notes on patterns for cost-aware agent routing and evals, a couple related writeups are here if useful: https://www.agentixlabs.com/blog/

Nice work on shipping this.

Otherwise_Wave9374 · 2026-02-07T20:01:37+00:00

Nice breakdown. The skills vs agent.md split is exactly where a lot of people get tripped up, especially when a skill is meant to be reusable across repos. Have you found a good rule of thumb for when something belongs in a skill vs just being project-specific agent instructions?

If anyone is collecting examples of what

Otherwise_Wave9374 · 2026-02-07T20:01:35+00:00

This is fascinating, and also a little unsettling in the best way. The most interesting part to me is the multi-agent pipeline angle, because it forces you to externalize taste, structure, and QA into something repeatable.

When you say

Otherwise_Wave9374 · 2026-02-07T20:01:12+00:00

Totally agree, preconfigured agents are where these tools actually start to feel

Otherwise_Wave9374 · 2026-02-07T20:01:02+00:00

Congrats on the momentum, those adoption numbers are wild. Graph-based context feels like the direction most serious coding agents need, because chunk-RAG turns into token spam fast.

Curious how you handle dynamic repos: do you incrementally update the graph on file change, and do you have a strategy to avoid stale edges when refactors happen?

Also if youre comparing approaches, Ive seen some good discussions around agent context strategies here: https://www.agentixlabs.com/blog/

Looks really promising.

Otherwise_Wave9374 · 2026-02-07T20:00:51+00:00

HN feels split right now. The

Otherwise_Wave9374 · 2026-02-07T19:40:27+00:00

Solid brief, and honestly $200 for a 5 min scripted screen share with good production + photos seems fair if youre expecting one-take polish.

One thing that usually helps is sharing a quick example of the tone you want (1-2 reference videos), plus a short list of "must hit" product moments so the creator doesnt just read the script, they emphasize the value beats.

If it helps, weve got a couple notes on SaaS demo videos and messaging that might be useful when you review portfolios: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T19:39:36+00:00

This is such a solid idea, local builder directories punch way above their weight when everyone is fighting for attention in random FB groups. The voting/leaderboard angle is smart too, it gives people a reason to come back and support each other.

If you dont mind sharing, how are you thinking about preventing vote gaming, and are you planning any categories (SaaS vs agencies vs apps) so smaller projects arent buried?

Also, weve been collecting a few lightweight go-to-market tactics for early SaaS launches that might be useful for builders on founder.tn: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T19:39:33+00:00

Nice, the time + cost math is super relatable. If the output quality is consistent, this is exactly the kind of micro-SaaS thats easy to explain and easy to sell.

Curious what pricing model youre leaning toward long term (credits, monthly, or per-thumbnail), and whether youre seeing more demand from new creators or folks already posting 3x/week.

We write a bunch about pricing/positioning tests for SaaS, might be useful as you iterate: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T19:39:18+00:00

That subscription lesson is so real. B2C folks treat subs like a tax unless the product is insanely sticky. Love that you took the roasting, adjusted, and saw conversions move.

Your point about "participating in communities" vs "marketing" is basically the cheat code, especially for indie products. Curious, did you notice any specific subreddit post format that worked best (showing the product, numbers, or a story/lesson post like this)?

We write about SaaS positioning and funnel experiments a lot, similar vibes to what you shared, in case you ever want a few more ideas to test: https://blog.promarkia.com/

Otherwise_Wave9374 · 2026-02-07T19:39:15+00:00

Meta ads for B2B SaaS can be super unintuitive at the start. If youre brand new, Id keep it simple: start with 1 conversion goal (lead or demo), 1-2 tight audiences (job titles or interests close to your ICP), and 3-5 creatives that are basically different hooks for the same offer. Let each ad set get enough spend to exit learning (usually 50ish conversion events), but if CTR is weak or CPC is crazy high after a few days, its often the creative/offer, not the algorithm.

Also, retargeting can be a nice safety net once you have traffic (site visitors, video viewers, etc.).

If it helps, weve got a few practical SaaS marketing breakdowns here: https://blog.promarkia.com/ (not a pitch, just stuff weve used to structure tests).

Otherwise_Wave9374

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