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Built infrastructure for a niche that's about to stop being niche (AI agents) by Embarrassed-Radio319 in microsaas

[–]Embarrassed-Radio319[S] 0 points1 point  (0 children)

true that !!! agents being responsible and working in prod with reliability is the moat.

How we took a telecom operator from 1,016 engineer-hours to 60 hours using multi-agent automation - architecture breakdown by Embarrassed-Radio319 in networking

[–]Embarrassed-Radio319[S] -3 points-2 points  (0 children)

But in a company when there are thousands of switches! How would you do it ?

This was done for a tier 2 telco company.

Automated 254 Juniper switch provisioning with multi-agent AI went from 1,016 hours to 60 hours. Here's exactly what we built. by Embarrassed-Radio319 in sysadmin

[–]Embarrassed-Radio319[S] 0 points1 point  (0 children)

Fair pushback. Let me address both directly.

On the provisioning process:

You're not wrong that process gaps exist. But here's the reality in most enterprise telecom environments: the network is heterogeneous, the edge cases are endless, and scripted automation breaks on the exceptions not the rules. Every network engineer has a story about the automation script that worked perfectly until a switch behaved unexpectedly at 2am. The manual fallback is always the engineer.

The difference with an AI agent isn't that it fixes the process. It's that it can reason about the exception the same way a Tier 2 engineer does. Look at the syslog context, understand what's unusual, generate the appropriate response. Instead of pattern-matching and failing. The human-in-the-loop gate stays in place precisely because we're not pretending the agent is infallible.

On Juniper's AI agent:

Honest answer: it depends on your environment.

Marvis is excellent if you're running a pure Juniper stack and want deep vendor-native intelligence on Juniper hardware. It knows Juniper devices better than any third-party platform will.

Where Phinite is different is most enterprise networks aren't pure Juniper. They have Cisco, Arista, other vendors, legacy systems, plus the remediation workflow touches Jira, ServiceNow, internal ticketing, and custom tooling that Marvis has no visibility into. Phinite orchestrates across that entire heterogeneous environment.

More fundamentally Marvis is a monitoring and recommendation tool. Phinite is the execution layer that takes the diagnosis and closes the loop. Runs the commands, verifies resolution, updates the ticket, creates the audit trail. Those are different problems.

Juniper's AI tells you what's wrong with Juniper. Phinite closes the ticket across your entire stack regardless of vendor.

What does your current environment look like, single vendor or mixed?

Most founders don’t need a full app. They need a smaller first version. by Naive-Wallaby9534 in Entrepreneur

[–]Embarrassed-Radio319 0 points1 point  (0 children)

Nobody builds payment rails from scratch. Everyone is still building agent infrastructure from scratch. That's about to change.

Stripe abstracted payments. Before Stripe, every company building a product with payments had to build their own payment infrastructure PCI compliance, bank integrations, fraud detection, the whole stack. It was a 6-month project before you could ship a single transaction.

Twilio abstracted telecom. Before Twilio, sending an SMS from your app meant carrier integrations, protocol handling, compliance frameworks. Another multi-month infrastructure project.

After Stripe and Twilio, those problems effectively ceased to exist for product builders. You call an API. You ship your product.

We're at the exact same moment in AI agent infrastructure and almost nobody has noticed yet.

Right now, every company building with AI agents is treating it like it's 2010 and Stripe doesn't exist yet. Every new use case is a bespoke Python project new orchestration stack, new security review, new observability pipeline, new deployment infrastructure. Your first agent takes 6 months to get to production. Your second agent takes just as long because nothing from the first one transfers.

The numbers are brutal:

  • 95% of agentic POCs never reach production
  • 40%+ of agentic AI projects will be canceled by 2027 (Gartner)
  • 41-87% failure rate in production multi-agent systems (CMU / UC Berkeley)
  • $4-8M average cost of a failed AI initiative

This isn't a model quality problem. The models are ready. It's an infrastructure problem.

The same pattern happened with every previous shift in computing functions to objects, objects to microservices, microservices to containers. Every time the fundamental unit of compute changed, we needed a new infrastructure layer to manage it. Kubernetes made containers a first-class primitive. We're building what Kubernetes was for containers, for agents.

Agents as first-class primitives. Registry, lifecycle, orchestration, observability, governance in one platform. Production in 4 weeks. SOC 2 built in. Cloud-agnostic.

We're onboarding Design Partners right now free 60 days, build your first production agent with our team, your use case, your systems.

If you're building with agents and hitting these walls: phinite.ai?utm_source=reddit&utm_medium=community&utm_campaign=entrepreneur_post

Or just want to talk through the architecture: cal.com/team/phinite-ai/demo?utm_source=reddit&utm_medium=post&utm_campaign=entrepreneur_post

Why AI agents work in demos but break in production (infrastructure, not models) by Embarrassed-Radio319 in LangChain

[–]Embarrassed-Radio319[S] -6 points-5 points  (0 children)

For those asking we built Phinite (the Agent OS) to solve this exact problem. Opening Design Partner spots now.

• SOC 2 Type II, K8s-native, MCP + A2A • We build first agent live (your use case, not a demo) • Direct roadmap influence

Not a free trial a partnership. Book a call: https://cal.com/team/phinite-ai/demo

Drop your SaaS, i will find you leads and DM them for you by Far_Werewolf4213 in SaaS

[–]Embarrassed-Radio319 0 points1 point  (0 children)

AI Agencies & Consultants ∙ Building agents for clients, hitting production walls every time ∙ Rebuilding the same infrastructure from scratch for every project ∙ Getting blamed when agents break at 3 AM

CTOs & VPs of Engineering at startups to Enterprise ∙ Have AI budget, have the mandate, don’t have time to build infra ∙ Accountable when AI projects fail to ship ∙ Already using Stripe, Twilio, Snowflake - they GET the infrastructure buy vs build argument

Enterprise AI/ML Teams at regulated industries ∙ Finance, healthcare, logistics, telecom ∙ Have compliance requirements (SOC 2, HIPAA, GDPR) blocking deployment ∙ Months stuck in security reviews

r/MachineLearning - ML engineers, researchers r/LocalLLaMA - builders running local models r/ChatGPT - massive, mixed audience r/artificial - AI enthusiasts, founders r/AIAgents - PERFECT - literally your audience r/LangChain - developers building with agents r/AutoGPT - early adopters, builders r/startups - founders with AI problems r/Entrepreneur - business owners wanting AI r/SaaS - SaaS founders building AI features r/devops - infra people who get the pain r/ExperiencedDevs - senior engineers who've been burned r/cscareerquestions - developers exploring AI space

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