Elsner Technologies, a global software and web development agency, today announced the expansion of its AI agent development practice, aimed at helping enterprises move beyond isolated AI pilots and into production-grade business automation.
Most companies aren’t struggling to find AI use cases anymore. They’re struggling to get agents out of a demo environment and into a system that actually runs the business, safely, and without breaking three other workflows in the process. That’s the gap this expansion is built to close.
Why This Matters Right Now
Enterprise adoption of AI agents has moved fast this year. According to Gartner, 40 percent of enterprise applications are expected to embed task-specific AI agents by the end of 2026, up from under 5 percent in 2025. McKinsey’s State of AI 2025 report found that nearly two-thirds of organizations are now experimenting with agentic systems, though only about 23 percent are actively scaling one in at least one business function.
Organizations that get past the pilot stage report meaningfully higher returns on their broader AI investment. An IDC study commissioned by Microsoft, surveying more than 4,000 business leaders, found that companies see an average return of $3.70 for every $1 invested in generative AI, with financial services reporting the highest returns of any sector studied. Agent deployments sit downstream of that same investment curve, so the same discipline that drives generative AI returns tends to carry over.
None of that growth is evenly distributed, though. It depends heavily on how the agent gets built and governed, not just whether a company decides to build one.
The Part Most Vendors Don’t Mention
Here’s the uncomfortable half of the story. Gartner also predicts that over 40 percent of agentic AI projects, will be canceled by the end of 2027 citing escalating costs, unclear business value, and inadequate risk controls as the primary reasons. Notably, the model itself is rarely the problem. It’s usually the governance, ownership, and integration work around it that never got built.
There’s also a labeling issue worth naming directly. Gartner itself uses the term “agentwashing” to describe products marketed as AI agents that are really just assistants, chatbots, or rule-based automations with a new label attached. Buyers evaluate a demo, assume it will behave the same way inside a live ERP or CRM environment, and then discover the gap once real data and real edge cases show up. Elsner’s approach starts from the opposite direction: scope the workflow first, then decide whether an autonomous agent, a simpler automation, or a manual fix is actually the right tool.
What Elsner’s Expanded Practice Covers
Elsner’s expanded offering builds on its existing engineering base rather than launching a separate product line. Three areas define the scope of the practice.
Workflow-native agents, not chat widgets: Elsner builds agents that read and write directly into existing systems, ERP records, CRM pipelines, inventory data, support tickets, instead of sitting on top as a chatbot layer. This is where most in-house attempts fail. A model that can answer questions is not the same as one that can safely update a customer record or trigger a fulfillment action, and the engineering difference between those two things is significant.
Multi-agent orchestration for complex operations: For clients running parallel processes, order management, support triage, and demand forecasting at once, Elsner designs coordinated agent systems rather than a single general-purpose bot. Gartner named multiagent systems one of its top strategic technology trends for 2026, noting that they give organizations a practical way to automate complex business processes and create new ways for people and AI agents to work together. It’s increasingly where the real operational value sits once a business moves past a single simple use case.
Governed deployment from day one: Elsner’s standard approach builds in logging, audit trails, and human-in-the-loop checkpoints for high-risk actions rather than treating them as optional add-ons. This pairs with Elsner’s data engineering and MLOps services so agents run on clean, monitored pipelines instead of ad hoc data pulls, which is usually where “the agent gave a wrong answer” complaints actually originate.
How These Agents Actually Get Built
Elsner runs every engagement through a staged process rather than a single big-bang deployment. It typically looks like this:
- Workflow discovery: mapping the actual process, including where it breaks today, before writing a line of agent logic.
- Scoped pilot with a kill switch: a narrow version of the agent runs on a limited data set with a manual override always available, so failure modes surface early and cheaply.
- Staged rollout: access expands gradually across teams or data volume once the pilot clears defined accuracy and safety thresholds.
- Ongoing monitoring: usage, error rates, and edge cases get tracked continuously, not just checked once at launch.
It’s a slower start than a vendor demo promises. Usually worth it. That kind of staged approach is built to directly address the cost, business-value, and risk-control gaps Gartner identifies as the main reasons agentic AI projects get canceled, rather than discovering them after the agent is already live.
Where the Demand Is Coming From
Current demand is particularly strong across a handful of specific areas:
- Ecommerce operations: merchants wanting agents for inventory sync, order status handling, and product data structuring, work that pairs naturally with Elsner’s ecommerce development capability.
- ERP-adjacent automation: Odoo users asking for agents that can create records, summarize customer notes, or flag anomalies, an area covered under Elsner’s Odoo development services.
- Customer-facing automation: support and sales agents that need to hand off cleanly to a human, without losing context when a real person picks up the conversation.
- Subscription and SaaS operations: teams wanting agents to flag churn risk, summarize usage patterns, or route renewal follow-ups without waiting on a manual dashboard review.
Not every business needs a fleet of autonomous agents. Frankly, a lot of companies would get more value from fixing one broken handoff between two systems than from deploying an agent at all. Elsner’s discovery process is built to say that when it’s true, not to upsell a bigger engagement than the problem calls for.
Not Sure If an Agent Is the Right Fix?
Elsner’s team will walk through your actual workflow first and tell you honestly whether automation, integration, or a full agent build is the right call.
Built Alongside Elsner’s Broader AI Practice
This expansion doesn’t sit in isolation. Elsner’s agent development team works closely with its AI strategy consulting practice: one sets the roadmap and governance framework, the other builds and ships the agent itself.
That handoff matters more than it sounds. A roadmap without an execution team behind it stays a slide deck. An agent built without a roadmap tends to solve the wrong problem quickly instead of the right problem eventually.
“Most enterprises don’t need more AI ambition. They need someone willing to say no to the flashy build and yes to the boring integration that actually saves the operations team four hours a day. That’s the practice we’ve built.” Senior Executive, Elsner Technologies
The same executive added that the biggest predictor of a successful agent deployment isn’t the model chosen. It’s whether the client’s team already trusts the data the agent will be working from. Skip that step, and even a well-built agent inherits every existing data problem, just faster and with less human oversight to catch it.
That’s also why forecasting tends to be a natural extension point. Clients running demand or churn forecasting through Elsner’s business intelligence services already have a data layer an agent can trust, which usually cuts weeks off the discovery phase described earlier.
Support automation follows a similar path, just on the customer-facing side instead of the operational one. Teams already running Elsner’s conversational AI and chatbot work tend to move faster into agent territory, since the handoff logic between bot and human is something they’ve already worked through once.
About Elsner Technologies
Elsner Technologies has been building custom software, ecommerce, and enterprise platforms since 2006. Two decades of shipping production systems is the actual foundation behind this practice, not a talking point bolted onto an AI trend. Elsner’s engineering teams work across AI agent development, Shopify and Odoo implementation, SaaS development, and business intelligence, with delivery centers supporting both technical execution and long-term platform ownership. The firm has built its AI practice around a deliberately unglamorous principle: an agent that reliably does one thing well beats one that impressively does ten things unreliably.
Frequently Asked Questions
What kinds of AI agents does Elsner build?
Elsner builds task-specific agents that connect directly to a client’s existing systems, ERP, CRM, ecommerce platforms, and support tools, rather than standalone chat interfaces. This includes single-purpose agents and coordinated multi-agent setups for more complex operations.
How long does a typical agent deployment take?
It depends on scope. A single-purpose agent connected to one system can often ship in a few weeks. Multi-agent orchestration across several business functions usually runs longer, since it involves more integration and governance work upfront.
How does Elsner avoid the failure patterns Gartner describes?
Mainly by starting narrow. Every engagement begins with a scoped pilot on limited data, with a manual override built in, before any expansion happens. Cost, business value, and risk controls get defined upfront rather than discovered after launch, which is where most canceled projects run into trouble.
Which industries is this practice built for?
Elsner works most often with ecommerce businesses, Odoo and Shopify users, and mid-market enterprises running SaaS or ERP-heavy operations. The approach adapts to any business with clearly defined, repeatable workflows.
How do I get started?
Reach out through Elsner’s contact page to schedule an initial workflow review. The team will assess whether an agent, an integration fix, or a different approach fits your situation best before recommending a build.
