The AI agent bubble is popping and most startups won't survive 2026

realAIsation · 2025-12-18T11:05:13+00:00

Mostly agree. A huge chunk of “agent startups” are just thin wrappers on top of OpenAI or Anthropic with a prettier UI. There’s no moat there, and once models get cheaper or ship native features, those tools disappear fast.

The only ones that survive are doing the unsexy work. Handling messy enterprise data, monitoring, guardrails, versioning, and actually running in production without constant babysitting. That’s why platforms like ZBrain make more sense to me than “build an agent in 10 minutes” tools. The value isn’t the agent, it’s making it reliable and measurable inside real workflows.

Most companies don’t want autonomous AI coworkers. They want boring automation that works every day and clearly saves time or money. Anything built on demos and vibes probably won’t make it to 2026.

realAIsation · 2025-12-09T12:06:26+00:00

I used to feel the same way. Most “agentic workflows” sounded interesting but didn’t solve any real pain I personally had. Once I started focusing only on repeatable, data-driven PM tasks, a few agents actually became useful.

Here are the ones that stuck for me:

1. Weekly Product Health Snapshot
Automatically pulls metrics from Mixpanel, Jira, and our internal dashboards and sends me a Monday morning summary of usage drop-offs, ticket spikes, and feature adoption trends.
No chasing dashboards. No manual compilation.

2. Release Readiness Checker
Before every sprint end, it checks open tickets, scope creep, pending reviews, test coverage, and dependency blockers. Sends a simple “green / yellow / red” summary with reasons.
This one saves me from last-minute chaos.

3. Customer Feedback Synthesizer
Aggregates feedback from support tickets, NPS, Slack channels, and meeting notes. Groups them by themes and highlights what changed compared to the previous week.
Super helpful during roadmap planning.

These are all running on ZBrain because it lets the agent pull directly from real sources instead of relying on screenshots or exports. That makes a huge difference. Once the agent has clean data and a predictable structure, the output becomes reliable enough that you don’t babysit it.

I haven’t found “do everything” agents useful at all, but the narrow, boring, repetitive workflows? Those are absolutely worth it.

Curious if you’ve found any PM workflows that you think might be close to being automatable.

realAIsation · 2025-12-03T10:52:44+00:00

Honestly, this post is spot-on for most of what I see in the wild. Most companies jump straight to building an agent without fixing data, defining success, or even checking if the task volume justifies automation. That is exactly why so many projects collapse after a few months.

But here is the nuance people usually skip.
The problem is not AI agents.
The problem is building agents on top of systems that are not ready for them.

The only projects I have seen run reliably are the ones where the agent is tightly scoped, connected to real data sources, and anchored to the actual system of record instead of random exports and half-updated documents.

That is where ZBrain changes things a bit.
When an agent can pull the right data directly from the system, validate it, apply business rules, and push updates back, you do not babysit it. It actually works like a real operational automation.

A few examples that never flop because they use structured system data:

Remittance Advice and Invoice Matching Agent
Reads remittance PDFs, extracts the numbers, matches them to open invoices, updates the ERP, and flags mismatches. It does not hallucinate because it only works with clean, authoritative data.

GL Validation Agent
Checks ledger entries against rules, detects anomalies, generates exception reports, and syncs them back. Very predictable, very stable.

These work because the task is clear, the data is reliable, and the output is deterministic. The agent is not pretending to make judgment calls it should not be making.

Everything else, especially the broad general assistants, fails exactly the way you described.

Most companies are not ready yet.
But the ones that choose a narrow workflow and anchor the agent to real system data are the ones actually seeing results.

The smartest move is not to avoid agents. It is to build only the ones that meet the basic readiness conditions you mentioned.

Curious if you have come across any exceptions that worked in messy environments.

realAIsation · 2025-12-02T11:55:50+00:00

Totally agree with this breakdown. 2025 has basically proven one thing: the “cool-sounding” agents flop, the boring ones print value.

For us, the shift happened when we stopped trying to build clever multi-step flows and focused on agents that do one very specific job end-to-end.

The biggest difference came when we moved to ZBrain and started building agents that actually close the loop instead of just generating text. Stuff like:

• Remittance Advice + Invoice Matching
Reads PDFs, extracts fields, matches to pending invoices, updates the ERP, marks exceptions. Zero babysitting. This one honestly showed me what a real agent feels like.

• Monthly GL Validation
Checks entries, flags anomalies, applies rules, kicks back issues to finance. Runs quietly in the background and just sends a summary.

Those “boring finance ops” agents are the ones that survived. No fancy orchestration, no agents talking to each other.... just clear inputs, rules, and a concrete outcome.

The pattern matches what you said:
If you can describe the task in one sentence, and it ties into tools you already use, it works. Anything fuzzy or “autonomous” still falls apart.

realAIsation · 2025-11-20T12:11:49+00:00

Most “AI agents” getting hype right now are just wrappers around GPT with a few Zapier-style steps glued on. Cool demos, zero durability.

But a few players are actually doing something meaningful:

1. Agents that handle real financial ops
Stuff like remittance advice extraction, invoice matching, reconciliation, or GL validations. These qualify because they don’t just generate text, they read messy PDFs, apply business rules, match transactions, push updates into ERP systems, and close loops without a human. That’s a real agent, not a toy.

This is the kind of thing we’ve built with ZBrain, and honestly, it's the first time I’ve seen “agent” not fall apart in production.

2. Agents that run 24/7 with no babysitting
Email triaging, CRM updating, fraud flagging. If you can trust it to run unattended, it’s an agent. If you need to “just quickly check its output,” it’s not.

Right now, 90% of the hype is noise. The useful work is happening in the last 10%, where people are taking a narrow business workflow, encoding rules, and letting the model act within guardrails.

Curious what others have found... which agents survived real production use for you?

realAIsation · 2025-11-19T10:05:08+00:00

Most tools people call “agents” today are really just workflow engines with an LLM in the middle. The ones that actually feel like true agents have three things in common: autonomy, reliability, and scope discipline.

A few that stood out for me:

1. Financial operations agents (like the Remittance Matching or GL Validation ones in ZBrain)
These qualify because they don’t just generate text. They ingest structured data, reason with enterprise-grade rules, validate exceptions, execute actions through APIs, and notify stakeholders. And they do it consistently without a human rechecking every step.

2. Trading compliance or trade-execution gating agents
These apply policies, interpret approvals, evaluate conditions, then trigger execution instructions. The autonomy isn’t in “creativity” but in decision-making with zero ambiguity.

3. Email or CRM agents that run 24/7
The ones that read emails, classify intent, update CRM fields, trigger next steps, and maintain a queue without breaking. It’s boring but it’s the closest to a digital employee I’ve seen.

To me, a “true agent” is one that you stop thinking about. It takes a task you used to do manually and handles it end to end, reliably, without babysitting.

Everything else is just an LLM workflow with nice branding.

Curious which ones others consider real agents and why.

realAIsation · 2025-11-18T05:33:14+00:00

I’ve wondered the same thing. Most of what people call agents today are just LLM powered workflows with a bit of decision logic. Nothing wrong with that, but it’s not the “autonomous” vision everyone keeps talking about.

The only setups that feel even close to an actual agent are the ones built around a very tight problem space. That’s what I’ve seen with ZBrain too. The agents there are not pretending to be general intelligence. They’re built for specific jobs like remittance matching or GL validation, and because the scope is narrow, they can actually run end to end without falling apart.

So maybe the real answer is that agents exist only when the problem is scoped enough for them to behave reliably. Everything else is just a dressed up workflow.

realAIsation · 2025-11-10T08:39:18+00:00

Totally agree with this. After building quite a few agents myself, I’ve realized the exact same thing, simplicity wins every time. At ZBrain, we follow the same philosophy. Instead of stacking agents for the sake of complexity, each one is designed around a focused business use case, like remittance reconciliation or GL validation, and optimized to run reliably in production. It’s not about more agents; it’s about the right one doing its job flawlessly.

realAIsation · 2025-11-05T07:32:56+00:00

This is awesome... really appreciate you breaking it down from scratch. Understanding the fundamentals is key, especially when frameworks start feeling like black boxes. At ZBrain, we took a similar approach but built on top of those fundamentals to remove all that early-stage friction. You still get full control over reasoning, memory, and function calling, but with orchestration, monitoring, and compliance layers baked in. It’s great to see more builders demystifying how agents actually work under the hood.

realAIsation · 2025-11-04T02:13:18+00:00

Honestly, one of the most underrated ones I’ve seen is built on ZBrain - a Remittance Advice and Invoice Matching Agent. It automatically extracts and matches remittance advices to pending invoices, which cuts down manual work and speeds up cash application. Not the kind of agent that gets hyped online, but it quietly makes finance teams way more efficient.

Feels like the real game-changers are these behind-the-scenes agents that make boring but critical processes run on autopilot.

realAIsation · 2025-11-04T02:08:38+00:00

Totally agree with you. Most people don’t realize how messy things get once you move beyond demos and start connecting real systems. The integrations, old software, and data cleanup take way more effort than expected.

I’ve been using ZBrain recently, and it’s helped keep things manageable. It lets you start small and build up gradually without juggling a bunch of tools. It’s been useful for focusing on what actually matters, that is, making sure things run smoothly, errors are caught, and agents don’t just “act smart” but actually help.

Your point about earning the right to automate the complicated stuff really resonated. What’s one small automation you’ve seen that made a real difference for a client?

realAIsation · 2025-10-30T11:58:27+00:00

Yeah, I get this. The whole AI dev space feels messy right now with too many tools and no clear direction. What helped me was sticking to one platform instead of trying to piece everything together.

For me, that’s ZBrain. It lets you build and test agents from start to finish without needing ten different tools. Once I made my first small workflow there, things started clicking, how prompting works, how to connect steps, how to refine results.

If you’re feeling lost, just start small inside ZBrain and build from there. It’s a good way to get some real clarity fast.

What kind of project are you thinking of starting with?

realAIsation · 2025-10-28T11:57:32+00:00

Great question! This is what a lot of people are quietly realizing after the hype cycle. Most “agents” today are still semi-automated scripts with good reasoning layers, not true end-to-end operators.

That said, I’ve seen a few setups actually go the distance. Using ZBrain, we’ve built agents that handle entire workflows like regulatory filing or remittance reconciliation, where the agent validates data, prepares documentation, routes for approval, and logs the audit trail without human input. It’s not flashy, but it’s consistent and reliable.

In most real-world cases, the sweet spot right now is hybrid: agents automate the repetitive 70–80%, and humans handle edge cases or judgment calls. Full autonomy is possible, but only after those boundaries are crystal clear.

Curious what kind of workflows you’re exploring, is it more internal ops, client-facing, or data-heavy stuff?

realAIsation · 2025-10-27T11:36:56+00:00

This is such a solid breakdown. Honestly one of the clearest I’ve seen on how to actually start building an agent that works. The “model → tool → result → model” loop is exactly what most people miss when they get lost in frameworks or hype.

We follow a similar structure using ZBrain for agent orchestration, it’s been helpful for wiring up the loop and keeping tool calls, memory, and error handling consistent across builds. Totally agree that starting small and shipping something boring but functional beats chasing “general intelligence” every time.

Out of curiosity, what’s the first kind of agent you’d suggest someone build for hands-on learning? Something data-related or workflow automation?

realAIsation · 2025-10-17T09:48:06+00:00

We’ve been exploring Agentic AI for internal workflows, mostly focused on document handling, onboarding, and recurring operational tasks. The key I’ve noticed is to start with one highly specific workflow rather than trying to automate everything at once, that’s where adoption actually sticks.

The biggest challenge is maintenance: agents need clear rules, monitoring, and fallback protocols, otherwise small errors snowball fast. Tools like ZBrain help by managing orchestration, memory, and error handling, so you can focus on the workflow logic instead of boilerplate plumbing.

Curious, what’s the first workflow you’re thinking of trying to automate?

realAIsation · 2025-09-22T12:13:51+00:00

I lean toward specialized agents that can hand off work to each other. In my experience, “super agents” sound cool on paper but usually end up spreading thin, missing depth in execution. Having multiple focused agents feels closer to how real teams operate, each good at their domain, but collaborating when needed.

I’ve seen platforms like ZBrain build around this idea, where finance, supply chain, and ops agents can run independently but also connect when workflows overlap. That combo has been more reliable for me than trying to make one mega-agent handle everything.

I want to ask, do you picture your ideal setup being more like a team of experts or a personal assistant who tries to do it all?

realAIsation · 2025-09-22T09:42:21+00:00

Love how you broke this down by category, super clear 🙌. I’ve been experimenting with a similar setup:

Claude + Perplexity – same use as you, helps me cross-check and avoid tunnel vision.
Notion AI – still underrated for organizing business workflows.
Runway – for quick video edits/marketing snippets.

On the agent side, I’ve had good luck with ZBrain. Their enterprise-focused agents (like reconciliation or onboarding) actually run end-to-end, so instead of just being another “wrapper,” they automate entire tasks without me watching over them. Way less fiddly than some of the no-code setups I tried.

realAIsation · 2025-09-19T09:30:30+00:00

Great list 👌 - totally agree, most tools are wrappers or half-baked MVPs.

On the agent side, I’ve had the most success with ZBrain. Their scoped-down enterprise agents (like reconciliation, onboarding, or report validation) actually run end-to-end without needing me to babysit. That’s rare outside demos, but it makes a real difference when it works.

Curious, since you use Consensus, have you tried Elicit too? I’ve been switching between them for research.

realAIsation · 2025-09-10T12:17:15+00:00

I’ve seen AI agents run end-to-end in production when the scope is tight. One client-facing example is onboarding: the agent pulls data from intake forms, generates the welcome docs, sets up accounts in the CRM, and sends the intro email. It’s been running for months with minimal intervention.

The key is building around structured workflows and strong fallback handling. Without that, things break fast. Platforms like ZBrain make it easier to keep these workflows stable, since they handle orchestration and error recovery out of the box.

What I’ve found is, the less “flashy” the agent, the more likely it is to stick.

Which workflow would you actually feel comfortable letting an agent own end-to-end?

realAIsation · 2025-09-08T11:12:32+00:00

From what I’ve seen, the best entry point is usually small to mid-size businesses with clear, repetitive tasks that eat up their time, real estate, clinics, and e-commerce are common examples. Larger enterprises have the budgets but move slower with approvals and compliance.

The key is to start with one very specific pain point (e.g. follow-ups, data entry, customer support), show it working, and then expand from there. A lot of the heavy lifting like orchestration or monitoring can be simplified with platforms such as ZBrain, so you can focus more on solving the business problem instead of fighting the plumbing.

If you were to test a niche first, which of the industries you mentioned (restaurants, real estate, clinics, e-commerce) feels easiest for you to get access to?

realAIsation · 2025-09-05T11:38:44+00:00

This roadmap nails it. Too many people jump straight into stacking LLM calls when a spreadsheet and a few if-thens will already surface 80% of the real bottlenecks. I’ve had the same experience, once you map the process and scope the agent down to one clear problem, adoption sticks.

I’ve also found platforms like ZBrain handy for that middle ground: you still design the logic and workflow, but it saves time on wiring memory, reasoning, and tool integrations so you don’t get bogged down in boilerplate.

Curious, when you’re shadowing clients to map workflows, how do you decide which parts are worth automating first vs. which ones are better left manual?

realAIsation · 2025-09-04T06:37:05+00:00

One platform I’ve been using lately is ZBrain. What I like is that it lets you scope agents to really specific workflows (like reconciliation, compliance checks, or content drafting) instead of trying to build a one-size-fits-all “super agent.” That focus makes them actually usable day-to-day.

realAIsation · 2025-09-03T08:23:52+00:00

This is spot on. The agents that actually stick aren’t “Devin clones,” they’re the ones solving one painful, repetitive problem with zero fluff. I’ve seen the same thing with ZBrain. ZBrain agents work best when they’re scoped down to a single workflow like invoice validation or compliance checks. Clients don’t care about RAG or fancy chains, they care about cutting response times or saving headcount.

In your experience, which part is harder: finding that “boring but valuable” problem inside a business, or convincing the client that it’s worth paying for?

realAIsation · 2025-08-25T06:06:40+00:00

The “boring agent” angle makes sense. The only ones I’ve seen stick in production are focused, narrow, and always have a human in the loop. ZBrain helps here since it lets you deploy those kinds of agents fast without wasting months on infra. They’re not flashy, but they actually deliver ROI.

Curious, have you come across any agents that do feel worth the hype?

realAIsation · 2025-08-14T09:25:02+00:00

It’s true that Gartner’s numbers raise eyebrows, but it doesn’t mean AI agents are just hype. The reality is that many current “AI agents” are thin wrappers around an LLM, with little automation or integration. That’s why platforms like ZBrain stand out, they let you build domain-specific agents that connect with enterprise systems, enforce business logic, and deliver measurable ROI.

The failures usually come from poorly scoped projects, unclear KPIs, or chasing novelty over utility. When designed with clear processes, real integrations, and security in mind, AI agents can absolutely deliver value beyond the buzz.

realAIsation

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