The One Technologies Blog

AI-Powered Software Testing: How QA Teams Are Changing

2026-09-23T11:11:46Z

Every QA team seems to be using AI now. But many teams are still figuring out what that means in practice.

According to the World Quality Report 2025-26 from Capgemini, Sogeti, and OpenText, 89 percent of organizations are piloting or deploying generative AI in quality engineering. Roughly 1 in 7 have actually scaled it past the pilot stage. That gap between trying AI and running on it is where most of the real story sits, and it's exactly where software testing and QA services built around this shift earn their keep.

What AI actually does inside a test suite

Self-healing tests top the list. A UI element moves or gets renamed, and instead of the test breaking, the system adjusts the locator automatically and keeps running. Anyone who's spent a Monday morning fixing dozens of broken tests after a routine UI update knows the problem this can solve.

Test case generation from natural language is the other major use case. Describe a user's flow in plain language, and the system drafts the test cases instead of someone writing them line by line. Intelligent test selection and prioritization rounds it out, running the tests most likely to catch a regression first instead of running the entire suite top to bottom every time.

Generating tests isn't the same as testing the right things

72 percent of QA professionals now use AI to generate tests or optimize test scripts. That's a genuinely high adoption number, and it is also where the story gets more complicated than the headline suggests.

Many teams are using that capability to increase test volume: more test cases from requirements and more scripts from user stories. Fewer teams are checking whether that expanded coverage actually maps back to what the software is supposed to do. A suite that quadruples in size without a traceability model, behind it is a maintenance bill nobody budgeted for, arriving a few months later disguised as flaky CI runs, not the productivity win it looked like on the dashboard.

The QA role itself is shifting, not just the tools

41 percent of organizations report that their QA teams are evolving into quality engineering teams, with a greater focus on orchestration across pipelines, environments, and data rather than simply running test cases. 38 percent now involve business analysts directly in test creation, widening who writes and owns tests beyond the automation engineers who used to own that work alone.

That's a bigger shift than a new tool in the stack. It changes who is accountable for quality and means that testing knowledge once concentrated within QA is spreading into product and business roles.

The adoption curve isn't as smooth as it looks

In 2023, 31 percent of organizations reported not using generative AI in quality engineering. That fell to 4 percent in 2024, reflecting a sharp increase in adoption. In 2025-26, that figure rose again to 11 percent, suggesting that some teams may have encountered challenges while moving from experimentation to broader adoption.

That small reversal is worth paying attention to. It's not evidence that AI in QA is failing. It's evidence that piloting something and operationalizing it are two different stages, and moving from one to the other without the groundwork is exactly how a team ends up back at zero.

What separates the teams that actually scale it

Two factors stand out in the data: the quality and connectivity of an organization's underlying data, and the regulatory or compliance requirements that can affect the move from pilot to production. Teams with fragmented test data across disconnected tools can struggle to move beyond the pilot stage, regardless of the AI tooling they use.

Experience matters more than team size. Automation engineers with five or more years of experience report higher rates of AI to use within their teams than newer team members. This may suggest that experience and knowledge play a role in determining which parts of the testing process teams are comfortable handing off to AI.

What this looks like in a working QA team

A test suite flags a failure after routine deployment. Self-healing catches half of it automatically, adjusting a renamed button before anyone notices. The remaining failures can be prioritized, while AI-assisted root cause analysis can help identify the likely commit before a team member investigates the logs.

The team that built that traceability model six months before the AI rollout is the one seeing real time savings now. The team that skipped straight to test generation is the one drowning in a suite twice the size with no clearer picture of what is actually covered. If you're trying to close that gap, The One Technologies can help build the QA foundation needed to support effective AI-driven testing.

Build vs Buy Software: A Practical Decision Framework

2026-09-18T13:25:21Z

Most build-vs-buy decisions get made by comparing the wrong two numbers: a monthly subscription fee against a development quote. Neither one tells you what the software actually costs to own.

Total cost of ownership is where both paths get underestimated, usually by a factor of two to three. Build costs hide in cloud bills, headcount, and the opportunity cost of engineers not working on something else. Buy costs hide in implementation, renewals, and per-user pricing that creeps up every year. Getting this decision right starts with custom software development conversations that price out five years, not five months.

What buying costs over five years

A $300-a-month SaaS tool looks cheap next to a development quote. Run it out five years and that's $18,000 for one user. Multiply by a real team, add implementation time, training, and the integration workarounds nobody budgets for upfront, and hidden costs can add significantly to the license fee over that period.

None of that shows up on the pricing page. It shows up eighteen months in, when the tool that seemed to solve everything needs three other tools bolted on to cover what it doesn't do.

What building costs over five years

A $75,000 custom system with $12,000 in annual maintenance runs $135,000 over five years. That maintenance number matters more than people expect; ongoing upkeep typically runs 15 to 20 percent of the original build cost every year, whether anyone remembers to budget it after launch.

Faster development doesn't lower that bill either. AI-assisted development can reduce build time for some types of software, internal dashboards, integration glue, tools that used to take a quarter now shipping in days. But code built fast still needs an owner, tests, security review, and updates as dependencies shift. Speed at the start does nothing about the years of upkeep that follow.

Why the crossover point has moved a little

For genuinely narrow, low-stakes internal tools, building can make financial sense much earlier than it would for a large business-critical system. That shift is driven by AI-assisted development, reducing the time and effort required for some types of software.

It's also a smaller shift than the hype suggests. Most decisions that were bought five years ago are still being bought. Don't build your own email system, payroll platform, or accounting software. Those are solved problems with mature products already doing the job well.

A test that cuts through the noise

Track your team for two weeks. Every time someone says, "the system can't do that, so I have to work around it," write it down. Add up the hours lost to those workarounds across the whole team.

More than ten hours a week spent working around a tool limitations is a real signal that the tool doesn't fit how the business actually runs. Under that, the workaround is probably cheaper than a rebuild. Over it, the off-the-shelf product is quietly taxing the business every single week.

Where building earns its cost

Custom software makes sense when the workflow it supports is the actual competitive differentiator, not a commodity functions every business in the industry needs the same way. A proprietary process nobody else has, deep integration with systems, a vendor was never going to prioritize, or a scale where per-user SaaS pricing becomes genuinely punishing. Those are the cases where the five-year math tips toward build.

A concrete example

A logistics company running 200 users on a per-seat SaaS routing tool hits a wall the SaaS vendor never built for: routing rules specific to a regional fleet contract the vendor has no reason to prioritize. Workarounds pile up. Support tickets pile up with them.

Run the five-year math and the picture changes. The SaaS license alone runs past $400,000 over five years at that headcount, before counting the workaround hours. A custom routing engine built around the company's actual contracts costs more upfront but removes the per-seat scaling entirely, and the crossover point arrives well before year five once those workaround hours get counted honestly.

Everything else, the standard, solved, non-differentiating parts of the business, still belongs to a proven platform. Getting that split right is the actual decision, not a blanket preference for one path over the other. If you're weighing this for a real system your business runs on, The One Technologies' software consulting team can run the five-year numbers with you before you commit either way.

GoHighLevel API Integrations: What Can You Automate Beyond the Native Features?

2026-09-14T12:27:59Z

Close a deal in GoHighLevel, and the CRM knows about it instantly. Your accounting software doesn't, unless something's built to tell it.

GoHighLevel ships with over 50 native integrations and a workflow builder that handles a lot on its own. But the moment a business needs to talk to a tool outside that list, payment processors, fulfillment systems, internal databases, the native features stop being enough, and that's exactly where the platform's REST API and webhook system take over. Teams working with GoHighLevel developers usually reach this point within the first few months of running a real business through the platform, not years in.

What's actually native, and where it stops

The built-in integrations cover the obvious ground: Stripe for payments, Google and Outlook calendars, Facebook and Instagram lead forms, a handful of common tools most agencies already use. For a lot of setups, that's genuinely enough.

It stops being enough the moment a business runs something GoHighLevel never built a native connector for. A custom fulfillment system, an internal inventory database, a niche piece of software a client's been using for a decade. None of that shows up in the native integrations list, and the workflow builder alone can't reach it.

Webhooks, the API, and Zapier aren't interchangeable

Webhooks are direct, real-time, one-way notifications. GoHighLevel fires one the instant something happens, a new contact, a booked appointment, a payment received, and whatever's listening on the other end reacts immediately. Fast, but one-directional only.

The REST API works the other way. It lets an external system read or write GoHighLevel data on demand, pulling a contact record, updating an opportunity stage, creating a calendar event programmatically. Combine both, and you get real two-way sync: a webhook tells your system something happened, your system processes it, then calls the API to write the result back into GoHighLevel.

Zapier sits in between, useful for straightforward, lower-volume automations without writing code, but it adds latency from polling intervals and per-task costs that add up fast at real volume. For a handful of automations a month, Zapier is fine. For hundreds of events a day, webhooks and the API do the same job faster and cheaper.

What this unlocks

Two-way sync with accounting software tops the list. A deal closes in GoHighLevel, and a webhook pushes that event to an invoicing system automatically, instead of someone re-entering the same deal by hand into QuickBooks a day later.

Custom payment processors outside Stripe become usable too, triggering a GoHighLevel workflow the moment a payment clears somewhere else entirely. Fulfillment systems can sync order status back into a contact's record. So, a support rep sees the shipping status without opening a second tool. Internal databases that predate GoHighLevel by a decade can plug into the same pipeline without a business rebuilding its entire tech stack around one CRM's native feature list.

A concrete example

An agency wants a Slack alert the instant a lead crosses $10,000 in deal value, so a partner can jump on the call personally instead of finding out during Monday's pipeline review. GoHighLevel's native features don't reach Slack directly for a trigger that specific.

A webhook fires the moment the opportunity value updates, a small script checks whether it crossed the threshold, and Slack gets the alert within seconds. That's a five-minute conversation in a sales meeting turned into a permanent piece of infrastructure, built once and running silently in the background from then on.

Where DIY webhook setups quietly fail

GoHighLevel retries failed webhook deliveries automatically for a period, but that's not a substitute for your own error handling and logging on the receiving end. Relying only on GoHighLevel's retry mechanism means a silent failure just looks like nothing happened, with no record of what got dropped or when.

Payload mapping trips people up too. The JSON a webhook sends has to line up field by field with what the receiving system expects, and a mismatch rarely throws an obvious error. It quietly writes the wrong data into the wrong field instead, and nobody notices until a report looks off weeks later.

Getting the integration layer right

Native features handle the common cases well. The API and webhooks handle everything a business actually built its process around before GoHighLevel entered the picture, the accounting tool, the fulfillment system, the database nobody wants to migrate off of.

That layer is also where a rushed setup causes the most expensive kind of failure: the silent kind, where data just stops flowing and nobody notices for weeks. If your GoHighLevel setup needs to talk to systems the native integrations don't cover, hire a GoHighLevel developer who builds the error handling in from the start, not after the first silent failure gets discovered.

Multi-Agent AI Systems: The Next Evolution of Automation

2026-09-11T04:58:26Z

One AI agent can answer a question. It takes several working together to actually run a process.

That shift is already the dominant pattern. Google Cloud's 2026 AI Agent Trends Report found that networks of specialized agents collaborating on a task, not single agents trying to do everything, now define how enterprises deploy AI in production. Businesses working with AI services providers are seeing the same pattern up close: a single agent gets you a demo. A team of them gets you a working process.

What makes a system multi-agent

One agent doing everything eventually gets sloppy. Ask it to research a topic, write the draft, check the facts, and send the email, and quality drops somewhere in that chain, usually right where nobody's watching.

Split that into specialized roles instead. A research agent gathers the data. A writing agent drafts from it. A review agent checks the draft against the source material. A dispatch agent sends it once everything checks out. Each one does a narrower job, and does it better, then hands off to the next.

Where a single agent hits its ceiling

Take something as ordinary as a refund request. Check it against the return policy. Confirm the item's actually back in inventory. Calculate the amount owed, factoring in a partial return or a promo code. Update the CRM. Notify the customer. Flag it if anything's outside normal range.

A single agent juggling all of that starts dropping details around step four. Split those into four or five specialized agents, each handling one piece and passing context to the next, and the failure rate drops considerably. Specialized handoffs simply fail less than one generalist trying to hold six jobs in its head at once.

What this looks like inside a real business

Procurement is a common one. A request agent captures what's needed and from where. An approval agent routes it based on spend thresholds. A vendor-communication agent handles the actual back-and-forth on pricing and delivery. Nobody's manually forwarding emails between three departments anymore.

Sales pipelines work the same way. A qualification agent scores the lead. A CRM agent logs the activity and updates the stage. A scheduling agent finds a slot and sends the invite. Three narrow jobs, done well, instead of one system trying to be a generalist and doing all three adequately.

The part vendors don't lead with

Multi-agent systems cost more to run. Passing context between multiple models burns three to ten times the tokens a single agent would use for the same task, and that shows up on the bill every month, not just at setup.

Governance lags behind the excitement too. Gartner expects more than 40 percent of agentic AI projects to get canceled by the end of 2027, mostly from escalating costs, unclear business value, and risk controls nobody built in from the start. Only about 21 percent of organizations report having a mature governance model for any of this. And trust in fully autonomous agents actually dropped over the past year, from 43 percent down to 22 percent, as more companies got burned by systems making decisions nobody had reviewed.

What keeps it from turning into chaos

An orchestration layer matters more than any individual agent. Something has to decide which agent handles what, catch it when a handoff fails, and keep a record of every decision made along the way.

High-stakes steps still need a person in the loop. A refund under $50 can run on its own. A $5,000 vendor contract shouldn't clear without someone actually looking at it first. The system that scales well is the one built with that line drawn on purpose, not the one that automates everything just because it can.

Logging matters just as much as the line itself. When an agent makes a call that turns out wrong, someone needs to trace exactly which agent decided what, and why, without digging through five separate systems to reconstruct the sequence.

Starting without overbuilding

Two agents handling one real process beats ten agents handling nothing reliably. Pick a workflow with a clear start and end, build the two or three agents it actually needs, and watch it run for a few weeks before adding a fourth.

Most failed multi-agent rollouts had nothing wrong with the underlying concept. Someone built the org chart before building the first working handoff, and everything downstream inherited that mistake.

If you're ready to scope something narrower and actually get it running, hire AI developers who'll start with the one process worth automating first, not all of them at once.

RAG vs Fine-Tuning: Which AI Approach Is Better?

2026-09-07T06:38:35Z

An LLM's knowledge freezes the day training ends. Your company's data doesn't stop moving that same day. That gap is the entire reason this question exists.

Two ways to close it dominate the conversation. Retrieval-augmented generation, RAG, hands the model your current documents at the moment it answers. Fine-tuning bakes knowledge and behavior directly into the model's weights ahead of time. Over 70 percent of enterprise AI teams now lean on RAG as their primary technique, while fewer than a quarter rely on standalone fine-tuning, and the reason shows up the moment you look at cost and freshness side by side. Teams working with AI services providers usually end up choosing based on one question: does the answer change next week, or does it stay the same for years?

What each one actually does

RAG searches a knowledge base at the moment someone asks a question, pulls the most relevant documents, and hands them to the model alongside the query. The model reasons over what it was just given instead of relying purely on what it memorized during training.

Fine-tuning takes a base model and retrains it on a curated dataset, adjusting the weights so the behavior, vocabulary, and knowledge get baked in permanently. No retrieval step at inference time. The model just already knows, or thinks it does.

Where the accuracy numbers land

A well-tuned RAG pipeline hits 85 to 90 percent answer accuracy on domain-specific knowledge bases, according to a 2024 Applied AI Institute study. Get the chunking and retrieval sloppy, though, and that number can fall to 10 to 40 percent. Implementation quality drives that swing far more than the underlying technique does.

Fine-tuned models tend to hallucinate more on facts they didn't see clearly during training, since they're recalling from parameters rather than reading a source document in front of them. Where fine-tuning pulls ahead is consistency: structured output, a specific tone, a rigid format the business needs every single time. RAG can't guarantee that as reliably.

The cost looks nothing alike

A production RAG system serving 10,000 queries a day against a 500,000-document knowledge base typically runs $4,000 to $9,000 a month, covering vector database hosting, embedding refreshes, and inference. Per query, that lands around half a cent.

Fine-tuning flips the cost curve. A LoRA fine-tune on a 13-billion parameter model with 50,000 examples costs roughly $400 to $1,200 per training run, a one-time hit rather than a recurring bill. Full fine-tuning on a much larger model can climb past $35,000 for a single run. Cheap per query afterward, expensive to get there, and expensive again every time the underlying knowledge needs updating.

When RAG is the right call

Pick RAG when the answer changes: pricing, policy documents, inventory, anything tied to a date. Regulated industries lean toward it too, since a retrieved document is traceable back to its source, which matters when a compliance team asks where an answer came from. A fine-tuned model's weights can't produce that kind of audit trail.

When fine-tuning earns its cost

Fine-tuning wins when the task is narrow, repetitive, and needs a specific structure every time. Extracting the same five fields from thousands of invoices. Classifying support tickets into a fixed set of categories. Matching a company's exact tone across every generated response. None of that benefits much from pulling in fresh documents, since the underlying pattern rarely changes.

What this looks like in practice

Banking and insurance deployments show the split clearly. RAG handles compliance research and customer-facing summaries, where the answer has to trace back to a specific policy document. Fine-tuning handles fraud risk classification and sentiment scoring on earnings calls, where the task is the same shape every single time and speed matters more than citing a source.

Neither technique replaced the other in that setup. They ended up running side by side, each handling the half of the problem it's actually built for.

The pattern most teams land on

Pure RAG or pure fine-tuning is increasingly the exception rather than the default. The pattern that's become standard in 2026 fine-tunes a smaller open model for behavior, vocabulary, and format, then sits it behind a RAG pipeline for the facts. Fast inference, a consistent voice, and answers a compliance team can actually trace back to a source, at the cost of maintaining two systems instead of one.

Getting that split right, what belongs in the model versus what belongs in the retrieval layer, is where most in-house teams underestimate the engineering work. If you're weighing this decision for your own systems, hire AI developers who've built both, and can tell you honestly which one your actual use case needs first.

What Is Model Context Protocol (MCP) and Why It Matters

2026-09-03T06:55:47Z

Before November 2024, connecting an AI model to your CRM, your database, and your internal wiki meant writing three separate, custom integrations. Connect it to five tools, and maintaining five one-off pieces of code that all break differently.

Model Context Protocol replaced that with one open standard. Anthropic released it as open source, and in under two years it's picked up 97 million monthly SDK downloads and running in production at 28 percent of Fortune 500 companies. Teams building custom AI services now treat it as a default building block rather than an optional add-on.

What MCP standardizes

Think of it as a common plug instead of a different cable for every device. Before MCP, every AI application needed its own custom code to talk to every tool it used. An assistant that pulled from Salesforce, Notion, and a Postgres database needed three separate integrations. Each with its own quirks, and each one broke independently when the underlying API changed.

MCP defines one protocol for that connection. Build an MCP server once for your database, and any MCP-compatible AI application can use it. Whether that application comes from Anthropic, OpenAI, Google, or a startup nobody's heard of yet. The integration gets built once instead of once per AI tool that wants to use it.

The problem it solves

Developers call this the N times M problem. N AI applications, each needing to connect to M different tools, means N times M custom integrations if everyone builds their own. Ten applications and twenty tools is 200 separate integrations, most of them doing nearly identical work in incompatible ways.

MCP turns that into N plus M. Build the tool's MCP server once, build the application's MCP client once, and every combination just works. That's the entire reason the ecosystem grew as fast as it did. The math got dramatically better for everyone building on it.

What this looks like in practice

Picture a support agent that needs to check an order status, look up a customer's account history, and file a return. Without MCP, that's three custom integrations, each maintained separately, each breaking on its own schedule when one of those systems ships an API update.

With MCP, each of those systems exposes an MCP server once. Order status, account lookup, return filing, all speak the same protocol. Swap the underlying AI model six months from now, and none of those three integrations need to be rebuilt. That portability is the actual point, not a side benefit.

What adoption looks like right now

The numbers move fast enough that any snapshot goes stale within months, but the direction is consistent. Over 10,000 MCP servers are published to public registries. The protocol moved to the Linux Foundation's Agentic AI Foundation in December 2025 for vendor-neutral governance, which matters because no single company controls where the standard goes next.

Real deployments back up the interest. Block cut token usage by 98.7 percent company-wide after adopting MCP through its internal Goose agent. Raiffeisen Bank reported a 40 percent improvement in risk assessment after integrating MCP into its risk management systems. PayPal runs MCP in production for payment processing and fraud detection, not a pilot tucked away in an innovation lab.

What still needs work

Production maturity hasn't caught up to the download numbers. A December 2025 survey of 300 senior technical leaders found only 41 percent had MCP servers in limited or broad production, well behind the hype suggesting near-universal deployment.

Security is the clearest gap. MCP's specification calls for OAuth 2.1 authentication, but only about 8.5 percent of live servers actually implement it. Teams are also running into what's being called MCP shadow IT: employees standing up unauthorized MCP servers inside a company's infrastructure, outside whatever review process IT thinks is in place. A protocol built for opening connections needs just as much attention paid to closing the wrong ones.

What this means for building AI applications now

If you're building anything that needs an AI model to reach outside its own context, a document store, a CRM, an internal API, MCP is very likely the right layer to build that connection through, instead of a bespoke integration that only works for one model provider.

The tooling is mature enough to build on. The security practices around it are still catching up, which means the implementation details matter as much as the decision to use MCP at all. If you're scoping an AI application that needs to connect to real systems, not just answer questions in a vacuum, hire AI developers who've already worked through where the protocol is solid and where it still needs a second look.

When to Hire a GoHighLevel Developer Instead of DIY

2026-08-31T06:24:43Z

GoHighLevel is genuinely built to run your whole business from one dashboard. That's also exactly why it's easy to set up wrong.

The platform folds a CRM, funnels, pipelines, email, and SMS into one login, and a solo agency owner can absolutely build a working setup without writing a line of code. The question isn't whether you can. It's whether the hours you'll spend, and the mistakes buried in a setup you don't fully understand yet, cost more than it would to hire a GoHighLevel developer who's already made those mistakes on someone else's account.

What's genuinely fine to build yourself

Starting from a pre-built snapshot and swapping in your logo, colors, and copy is well within DIY territory. So is a basic pipeline with a handful of stages, or a simple funnel that collects a name, email and sends one follow-up sequence.

If your business runs on one straightforward flow, capture a lead, send three emails, book a call, GoHighLevel's own templates and documentation get you there without much friction.

Where DIY setups quietly break

A2P 10DLC registration is the one that catches almost everyone. Every business sending automated SMS in the US has needed to register since February 2025, and the failure mode is silent: messages fire correctly inside GoHighLevel's interface, look sent, and never actually reach the contact, because the carrier blocked them behind the scenes.

The registration details have to match exactly too. A legal business name that says "LLC" in your GoHighLevel settings but "L.L.C." on your IRS paperwork is enough to trigger a rejection. Get approved, then let your live message content drift from what you registered, and carriers can block the campaign after launch, sometimes weeks after you thought the hard part was done.

Where the hours quietly add up

Sub-accounts and white-labeling for agencies fall into this category. Setting up one client account is manageable. Replicating that setup cleanly across twenty client sub-accounts, each with its own branding, domains, and permissions, turns into a project that eats a week of admin time most agency owners would rather spend on client work.

Complex workflows follow the same pattern. A single automation with one trigger and one action is simple. A workflow with five branching conditions, three different trigger sources, and a webhook firing into Stripe on the other end is where a misconfigured step quietly breaks a whole sequence, and finding which of the five branches failed can eat an entire afternoon.

API integrations sit in this bucket too. Connecting GoHighLevel to Stripe for payments, a custom lead source, or an external booking tool involves webhooks and authentication that don't have a drag-and-drop equivalent in the platform's UI. Get the field mapping wrong on one of those, and a payment can process without ever updating the contact record it was supposed to trigger.

A simple way to decide

Ask what happens if this breaks silently for two weeks before anyone notices. A basic funnel with a typo costs you a little polish. A broken A2P campaign costs every missed appointment reminder and every lead nurture sequence that quietly stopped firing, for however long it takes someone to notice the silence.

If the setup touches compliance, payment processing, or more than a handful of client sub-accounts, that's the threshold where a mistake stops being cheap to fix. Below that line, building it yourself is a reasonable way to learn the platform. Above it, the learning curve gets expensive fast.

What a developer actually adds

It's not just familiarity with the interface. A developer who's registered a hundred A2P campaigns knows which vague answer on the form triggers a rejection before they submit it. One who's built dozens of client sub-accounts has a checklist that catches the branding detail a first-timer forgets on client seventeen.

Marketing logic matters just as much as the technical setup. Understanding what actually moves a lead through a funnel, not just how to build the funnel, is what separates a working system from one that's technically configured correctly but converts nothing.

Making the call

Simple, single-client setups are worth building yourself, at least at first. Anything involving SMS compliance, multiple sub-accounts, or automations with real money moving through them is where the DIY hours start costing more than the hire would have.

If your setup has grown past what the templates were built for, The One Technologies' GoHighLevel developers can take over the parts where a silent mistake actually costs you clients, not just polish.

Agentic AI: The Next Frontier of Autonomous Systems

2026-08-27T05:09:00Z

A chatbot answers your question. An agent decides what to do next, then does it.

That's the line agentic AI crosses, and it's why 2026 feels different from the chatbot wave of a few years back. These systems don't just respond. They plan a sequence of steps, pull the data they need, and carry out a decision, often without a person approving every move. The One Technologies' AI services team builds systems like this for companies that are past the chatbot stage and want AI that actually moves work forward.

What actually makes AI agentic

Most AI tools before this wave did one thing. Classify a document, predict a number, generate a paragraph. You asked, it answered, and you decided what to do with the answer.

Agentic AI closes that last gap. Give it a goal, say, cut cart abandonment by 15 percent, and it can pull the data, test a few approaches, adjust send times, and report back. No one has to approve each step along the way.

The building blocks aren't new. Machine learning, natural language processing, computer vision, all of it existed before. What changed is stringing them together with memory and a planning loop, so the system carries context from one action into the next instead of starting fresh every time.

How agentic AI differs from RPA

RPA scripts follow a fixed set of steps. Click here, copy this field, paste it there. Change one input outside its expected range, and the bot breaks.

Agentic AI doesn't run off a fixed script. It has a goal and a set of tools, and it decides which tool to use and in what order, based on what it actually finds. Point it at a messy inbox instead of a clean form, and it can still work out what to do next.

That's also why it takes more engineering to build correctly. A broken RPA bot fails loudly and stops. A poorly built agent can keep running and keep making decisions you didn't intend, which is exactly why the guardrails matter more here than they ever did for RPA.

Where it's already making decisions

This isn't a future-tense conversation. A few places it's already live:

Inventory systems that reorder stock and adjust pricing based on real-time demand, no purchasing manager in the loop for routine restocks.
Fraud systems that freeze a suspicious transaction and route it for review the moment it happens, instead of flagging it for someone to check the next morning.
Support systems that resolve a ticket start to finish, and only escalate the ones that genuinely need a person's judgment.
Scheduling agents that book, reschedule, and confirm appointments across calendars without anyone touching a single invite.

What goes wrong when you skip the guardrails

Handing decisions to a system that plans its own steps is powerful. It's also where most agentic AI projects go sideways.

An agent with no spending cap can burn through an ad budget in an afternoon. One with no escalation rule can approve the same refund twice. Usually the failure mode is a confident agent running unsupervised, not a wrong answer.

Every agentic system needs a boundary: what it's allowed to decide alone, and what gets flagged for a person first. That boundary is the actual engineering work. The automation part is the easy half.

What a real deployment looks like

Start small. Pick one decision, not ten.

Define the decision precisely, such as approve refunds under $50 without review.
Set the escalation rule for anything outside that range.
Connect the agent to the systems it actually needs, your CRM, your inventory feed, your support desk.
Watch its first 100 decisions closely before you widen its scope.

Most teams that skip straight to a wide rollout end up walking it back within a month. The ones that start narrow and expand gradually rarely have to.

Where this fits your business right now

Not every process needs an autonomous agent. A rule-based workflow still beats agentic AI for anything predictable and low-stakes. Agentic AI earns its place where the decision is repetitive, time-sensitive, and involves pulling data from more than one place before acting. The right AI development partner builds these systems around a single business goal at a time, using custom algorithms and predictive models trained on your data, not a general-purpose bot that tries to do everything and ends up doing none of it well.

The Conclusion

Agentic AI is software that acts on your data instead of waiting for someone to read a dashboard and decide. That's a different kind of tool, and it deserves a build process that treats the guardrails as seriously as the automation itself.

If you're ready to move past pilots and proofs of concept, hire AI developers at The One Technologies to scope a system built around one decision your business makes every day, and build it to actually run on its own.

AI Readiness Assessment: Is Your Company Actually Ready for AI Adoption?

2026-08-24T09:48:41Z

MIT's 2025 State of AI in Business report found that 95 percent of generative AI pilots delivered no measurable financial return. The models weren't the problem. Most of those companies never checked whether they were actually set up to use one. A proper AI readiness assessment catches that gap before it eats a budget cycle.

What 'AI ready' actually means

Five things, really. Your data. Your process. Your people. Your systems. And your patience for a second attempt if the first one flops.

None of that shows up when a leadership team gets excited in a strategy meeting. We have sat in plenty of those meetings. Excitement fills the room fast, and it tells you nothing about whether the export button on your CRM even works.

The five things worth checking before you spend a dollar

Data quality. Feed an AI system messy, scattered data and you get a messy, scattered result, just faster.
Process clarity. Can you walk someone through the exact steps you want automated, in order, without two people in the room disagreeing on step 3?
Team buy-in. Will people actually use this daily, or will it end up next to the CRM field nobody bothers filling in?
System integration. Does this plug into what you already run, or does it demand you rebuild half your stack first?
Budget for iteration. Round two and three cost money too. Is that money set aside, or did it all go to round one?

The excitement trap

A team can be genuinely excited about AI and still be nowhere close to ready for it. That excitement is a sales signal, not proof of readiness.

Here's the real tell. Ask what decision, specifically, you want the system to make. "We want to use AI for customer service" is still an idea, not a plan. "We want it to triage support tickets by urgency before a human sees them" is a plan. One of those you can scope this week.

A 10-minute self-check

Say these out loud with your team. No slides, no deck, just answers.

Name the one process you'd automate first, in a single sentence.
Where does the data for that process actually live, and can you export it today?
Has anyone on the team touched a similar tool before, even informally?
If the first attempt flops, is that a minor setback or does it end the budget for next year?

Fewer than three solid answers means the readiness work comes first. Three or four, and you're already ahead of most companies that jump straight to a vendor demo.

Readiness varies by department

Companies keep asking "are we ready for AI" as if one answer covers the whole building. Finance might have five years of clean, structured records. Support might have ticket notes scattered across three tools with zero consistent tagging.

Run the check department by department. You can be ready to automate invoice matching in finance while being nowhere near ready for an AI agent handling customer refunds, in the same company, in the same quarter. Pick the readiest process first instead of the flashiest one. That choice alone decides whether a pilot sticks around past month three.

What skipping this step actually costs

Failed AI pilots rarely die loudly. They just stop getting opened a few months in, once the demo excitement wears off. By then the budget's gone, the person who championed it has moved to a different project, and the next AI proposal gets a much harder no in that building.

A readiness check runs a few weeks. Compare that to a quarter of engineering time spent building around gaps nobody bothered to flag first.

What a real assessment looks like

It walks through your existing systems, maps where your data actually lives, and flags the process steps nobody's ever written down. Then it gives you a straight answer: ready now, ready after three specific fixes, or not the right fit yet.

That third answer is worth more than it sounds. A good consulting partner will tell you when you're not ready, because a rushed build that fails costs more, in cash and in internal trust, than eight weeks spent fixing a data pipeline first. Want that honest answer for your own operation? Hire AI developers who assess the fit before they touch a single line of code.

7 signs your legacy VBScript automation is a business continuity risk

2026-08-13T08:22:03Z

Somewhere in your environment, a .vbs file is probably running a task nobody thinks about anymore. Maybe a login script. Maybe a scheduled file move, or a nightly report dumping into a shared folder at 2am. It's been working for years, so everyone just assumes it'll keep working.

That assumption gets shakier every year. Microsoft's actively phasing VBScript out of Windows, and the businesses caught off guard are usually the ones who never bothered auditing what's still quietly running on it. If any of the signs below sound familiar, it's probably time to hire ASP.NET developers to look under the hood, ideally before something breaks on its own schedule instead of yours.

1. Nobody Currently on Staff Wrote It

The developer who built it left three jobs ago. Maybe longer. Whoever's stuck maintaining it inherited a file with zero comments, a version history nobody trusts, and a filename like final_v2_USE_THIS.vbs. That's a single point of failure wearing the name of someone who probably doesn't even remember writing it.

2. It Leans on a Windows Feature That's Already Being Deprecated

Microsoft announced VBScript's phased retirement back in 2023, and the clock's been running ever since. Right now it's still installed by default as an optional Windows feature. Sometime around 2027 that default flips, and VBScript stops being enabled unless somebody manually turns it back on. After that, Microsoft's plan is to pull it from Windows completely, DLLs and all.

If your automation depends on it, this isn't a someday item on the roadmap. Microsoft's holding the calendar, not your IT team.

3. Nobody Would Notice If It Silently Failed

Ask around your office. Does the script log errors somewhere a human actually checks? Does anything alert if it doesn't run at all, or dies halfway through? A lot of legacy VBScript automation has zero monitoring built in. It runs quietly, or it breaks quietly, and either way the first sign of trouble is a business process that just... stops happening. Sometimes for weeks before anyone connects the dots.

4. The Systems It Touches Have Changed Since It Was Written

File shares get restructured. Service accounts rotate. A vendor quietly changes an API without telling anyone who'd care. Old VBScript automation tends to hardcode paths, credentials, server names, because that's simply how people wrote scripts a decade or more ago. Each of those hardcoded assumptions sits there like a landmine, waiting on the next infrastructure change that nobody thought to check against it.

5. It Runs From One Machine, Tied to One Login Session

A lot of legacy automation gets scheduled under one person's Windows login, on one desktop, sitting under someone's desk. Not a server. Not a service account. That machine gets swapped during a hardware refresh and the automation just stops, no redundancy, usually no documentation pointing anyone back to where it even lived.

6. Security Can't Tell You What It Actually Does

VBScript's got a long track record as an attack vector, showing up in malicious documents and phishing payloads for years. That history is a big part of why Microsoft's walking away from it. Ask your security team what a given script touches, what credentials it uses, what data it moves around. If they can't answer cleanly, you've found an active blind spot, not a paperwork gap.

7. You've Already Had a Near Miss, and Treated It as a One-Off

A script failed last quarter. Somebody quietly patched the output at 6am before anyone else noticed, and everyone moved on with their day. Sound familiar? That near miss wasn't really a one-off. It was a warning, and it got filed under "inconvenience" instead of "signal." The next one tends to land at a worse time.

Why This Feels Urgent Now, Specifically

Every year VBScript stays on by default is a year businesses get to put off the inventory. That grace period is what's actually running out. Microsoft's own timeline puts the default-off switch somewhere around 2026 or 2027, full removal sometime after, exact date still unconfirmed. Nobody gets a calendar invite for this. It just stops working, usually mid-Windows-update, for a completely unrelated reason that has nothing to do with your automation.

This kind of risk doesn't announce itself with an alarm. It shows up as a business process quietly failing on some random Tuesday, root cause buried inside a script somebody wrote back in 2014 and never touched again.

What to Actually Do About It

Start with an inventory. Most companies genuinely have no idea how many .vbs scripts are running across their environment until somebody actually goes looking, and that first audit tends to turn up more than anyone expected. Rank each one by what actually breaks if it fails, not by how old the code looks. A script emailing a weekly summary matters a lot less than one quietly shuffling financial data between systems every night.

From there, tackle the highest-risk scripts first, usually rebuilding them in PowerShell or a proper application layer, and let the low-risk ones wait their turn. An experienced ASP.NET development company can help triage this fast. Chances are good they've already seen the exact login script or file-processing job keeping you up at night, and they'll know which ones need urgency and which can wait a quarter.