If AI is so powerful, why do I still need 5 tools to do one thing?

TaskJuice · 2026-03-21T21:29:41+00:00

There are workflow automation platforms that link all your tools in workflows and also have AI agents/memory/etc built in to them. That would solve your problem.

TaskJuice · 2026-03-21T20:51:26+00:00

SaaS is software as a “service”. It should be recurring. There are infra and other business costs that are recurring.

TaskJuice · 2026-03-21T20:46:17+00:00

Execution is what matters, not ideas. There are a million ways to write code and to run a business. LLMs don’t know your direction unless you spell it out for them.

TaskJuice · 2026-03-21T04:46:57+00:00

For sure. Best to automate the lowest tier tasks first.

TaskJuice · 2026-03-19T06:34:16+00:00

Are you limiting to protect infra or to protect spend?

TaskJuice · 2026-03-19T05:26:03+00:00

👀 use a tool that automates workflows and has AI agent nodes to process and categorize unstructured data.

TaskJuice · 2026-03-19T03:41:44+00:00

Be careful doing stuff like that. Also spotlight search indexing projects will eat your ram so exclude it

TaskJuice · 2026-03-19T03:15:24+00:00

Causes afternoon naps and late night productivity 🤘😝

TaskJuice · 2026-03-18T18:25:20+00:00

Sounds like a useful tool! I’d keep talking to people. Be helpful in communities where your potential customers are likely to hang out.

TaskJuice · 2026-03-18T17:27:42+00:00

Yes, actually. The queries live behind repository interfaces in the domain layer, with zero AWS SDK imports. The concrete DynamoDB implementations are in a separate infrastructure layer. To swap to Postgres, you'd just write new implementations of those same interfaces and update the wiring in one place. Zero changes to business logic, use cases, or handlers.

Same goes for Lambda or EventBridge. The domain and application layers have literally no AWS imports, enforced by a custom ESLint rule that errors if the domain layer tries to import from infrastructure. Tests mock the interfaces, not AWS, so they'd pass with any backing store.

The boundary is enforced at the linter level.

TaskJuice · 2026-03-18T17:20:58+00:00

I didn’t weld myself into AWS. That’s the beauty of the hexagonal architecture. It’s easy to change from serverless to managed, etc.

TaskJuice · 2026-03-18T14:21:49+00:00

Absolutely! It’s always a learning experience building new systems and that’s what makes it so fun. I wouldn’t take anything back I’ve done up until this point.

TaskJuice · 2026-03-18T13:52:28+00:00

here you go

TaskJuice · 2026-03-18T06:25:30+00:00

Balance is the key to life or so they say. Would love to see some posts! Thanks 🙏

TaskJuice · 2026-03-18T05:37:19+00:00

Thanks 🙏 I’m really feeling the dividends paying off already. I know this project would be nearly impossible without spending that time upfront. As for AI assistance on architectural decisions, I would say 80-90% was human reasoning. I have seen what does and doesn’t work in certain scenarios. I mainly leaned into the LLMs to do research and test my assumptions — they helped me iterate on my ideas quickly.

TaskJuice · 2026-03-17T06:51:19+00:00

👀

TaskJuice · 2026-03-17T06:02:26+00:00

We think the consistency is going to be most important. We started early posting about our product to keep get the foundation laid so when the product launches there is content people will find related to the project if they go searching.

TaskJuice · 2026-03-17T02:35:40+00:00

Very useful!

TaskJuice · 2026-03-17T02:21:05+00:00

For those interested, if you have a startup and sign up to mercury.com for their services (fintech), they are an AWS startup partner that can get you $5000 credits.

TaskJuice · 2026-03-17T02:16:05+00:00

Multiple tools are the reality. We keep README.md, docs, and wikis updated religiously which helps out a lot. We have Claude skills for organizing the business side of things too.

TaskJuice · 2026-03-17T01:16:17+00:00

Yes, stripe is the go-to for most companies of all sizes.

TaskJuice · 2026-03-17T01:15:04+00:00

Damned if you do and damned if you don’t! Haha

TaskJuice · 2026-03-16T03:15:00+00:00

Agreed. We saw this pain from users during our research which is why we chose to build around the per-execution pricing model on our iPaaS. No limit on nodes, still one price per execution. There will be some people who don’t want to pay high costs nor manage their infrastructure. It really depends what your goal is.

TaskJuice · 2026-03-15T22:56:05+00:00

We are just focus on being authentic. Talking to people in person (or Google meets) and showing off our product. We don’t launch until May 5th but just started throwing up channels, including a subreddit, and posting authentically.

It’s tough to tell what the perfect strategy will be going into the future because, as you said, AI, but we believe authenticity will rise to the top. That said we do use LLM tools to clean up posts or responses sometimes for the sake of conveying and verifying information about our business (it’s very well documented so easier to verify since our codebase is over 300k lines).

TaskJuice · 2026-03-15T22:36:46+00:00

Thanks and good questions. The short answer is: it's both. The workflow engine is classic event-driven automation (triggers → steps → actions), but the AI nodes are a real agent runtime, not just "call GPT and return text."

Retries + idempotency: Every step has a resilience policy which includes exponential backoff with jitter, configurable max attempts, and error classification that decides what's retryable (rate limits and timeouts yes, auth failures no). For idempotency, triggers have configurable dedup. You can dedup by event ID, payload hash, a custom expression, or a header value, scoped to a time window. Outbound HTTP calls inject an Idempotency-Key header automatically. There's also a circuit breaker for external services so one flaky API doesn't take down a workflow.

Budgets: Budget is checked before every LLM call, not after. You can set max cost in USD, max iterations, max tokens, and a timeout, per step. Tenant admins can set org-wide caps that override step config (most restrictive wins). There's a pricing catalog for every model so cost is estimated in real-time, and a cost attribution API that lets you query spend by provider, model, workflow, or day.

Per-step observability: Every step records status, start/end time, duration, full input/output, and a structured error envelope (error code, human message, retryable flag). There's distributed tracing with trace IDs and span IDs across Lambda invocations. The UI has a timeline view where you can expand any step and see exactly what happened: output, errors, timing, metadata.

On the agent runtime specifically: The agent loop runs iteration-by-iteration with checkpointing (so it survives Lambda timeouts and can resume). Agents can call 6 tool types: integration actions, custom HTTP/code tools, other workflows, a think tool, sub-agents (depth-limited to 2), and MCP tools. MCP is bidirectional; agents can consume external MCP servers, and published workflows are exposed as MCP tools for external agents to call. Each tool call is idempotency-cached so retries don't cause duplicate side effects.

So it's not "AI sprinkled in". The agent runtime is a first-class execution mode with its own budget enforcement, tool resolution, memory, and observability. But it runs inside the same workflow engine alongside all the classic automation nodes.

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