Vicedomini Softworks

AI & Automation

Avoid Vendor Lock In: Engineering First Enterprise AI Agents

10 September 2026

Avoid Vendor Lock In: Engineering First Enterprise AI Agents

AI agents are autonomous, multi-step software actors that plan, invoke tools, and execute end-to-end workflows without constant human prompting. For most companies, the right response is a focused pilot tied to a measurable KPI, not a wholesale rollout. Get the pilot right and the payoff is faster decisions, automated complex workflows, and knowledge work that scales without a proportional headcount increase.

TL;DR:

  • Most enterprise use cases succeed with multi-step planner architectures, which require continuous evaluation and monitoring of each step’s output.
  • Grounding AI agents in company data and systems is essential for reliable, long-term operation, making data grounding a top priority.
  • Human oversight becomes mandatory once actions involve substantial financial, legal, or reputational risks, requiring approval checkpoints and role-based controls.
  • Pilot projects should target high-volume, well-defined workflows like customer support or lead enrichment, with clear success metrics established upfront.
  • Using an engineering-first delivery approach improves long-term stability, compliance, and integration, especially when customizing for legacy systems or complex workflows.
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Table of Contents

What are agenti AI per aziende and how do they differ from chatbots and RPA?

An AI agent is a software system that receives a goal, decides on a sequence of actions, calls external tools or APIs to carry them out, and evaluates its own results before deciding whether to continue, retry, or stop. That loop, sometimes called agentic AI in vendor literature, is what separates it from the two technologies decision makers most often confuse it with.

A chatbot answers one turn at a time. Ask it a question, it responds, the interaction ends unless you prompt again. There is no persistent plan, no memory of intermediate steps, no capacity to decide that step three requires querying a database before step four can run. Robotic Process Automation (RPA) sits at the other extreme: it executes fixed, rule-based sequences with no reasoning at all. Change the layout of an invoice and a legacy RPA bot breaks, because it was never built to interpret, only to click through a script.

An agent occupies the middle ground and extends beyond it. It sets a plan against a stated objective, breaks that objective into sub-tasks, invokes the tools each sub-task needs, and reviews the outcome against the original goal before deciding on the next move. This lifecycle typically runs through five stages:

  • Goal setting: the agent receives an objective, either from a user, a scheduled trigger, or another system event.
  • Planning: it decomposes the objective into an ordered sequence of steps, adjusting the plan as new information arrives.
  • Tool invocation: it calls the specific systems needed, an email inbox, a CRM record, a database query, an external API.
  • Execution: it carries out each step, often chaining outputs from one tool into the input of the next.
  • Evaluation and feedback: it checks the result against the goal, flags exceptions, and either proceeds, retries, or escalates to a human.

Common triggers include an incoming email, a new CRM record, a scheduled batch job, or a webhook from another application. A simple example: a sales agent receives a new lead notification from the CRM, enriches the record by querying a public company database, drafts a personalised outreach email, and schedules it, only pausing for human approval if the deal size crosses a defined threshold. That last condition matters more than it looks. It is the difference between automation that saves time and automation that quietly makes commitments nobody signed off on.

Which agent architecture fits your use case?

Not every business problem needs the same level of agentic complexity, and picking the wrong pattern is the single most common cause of failed pilots. Four architectures cover most enterprise scenarios, in ascending order of capability and risk:

  1. Reactive agents respond to a single trigger with a bounded action, such as categorising an inbound support ticket and routing it to the right queue. They carry the lowest risk because the action space is narrow and predictable.
  2. Multi-step planners handle objectives that require several dependent actions in sequence, like the lead enrichment example above, adjusting the plan if an early step returns unexpected data.
  3. Multi-agent systems split a complex objective across specialised agents that collaborate, one handles research, another drafts content, a third checks compliance, coordinated by an orchestrator. Academic work on agent orchestration and evaluation shows this pattern scaling well for tasks that genuinely need division of labour, but it also multiplies the points where something can go wrong.
  4. Human-in-the-loop agents insert a mandatory approval checkpoint before any consequential action, financial transactions, customer-facing communications above a certain value, or anything touching regulated data.

Human oversight stops being optional once an agent’s actions carry financial, legal, or reputational consequences that are expensive to reverse. A procurement agent that can request a quote needs less supervision than one that can commit to a purchase order.

The architecture you choose dictates your testing and monitoring burden. A reactive agent can be validated with a fixed test set and periodic spot-checks. A multi-agent system needs continuous evaluation of both individual agent outputs and the orchestration logic that ties them together, plus service-level agreements that account for the latency of multiple sequential calls rather than one. Skipping this step is how a pilot that worked in a demo turns into an incident report six weeks after launch.

What should you require from an enterprise agent platform?

Model quality gets the headlines, but production readiness depends almost entirely on what sits around the model. Four capability areas separate a platform that survives contact with real operations from one that stays a proof of concept.

Business context and data grounding comes first. An agent that cannot see your CRM records, your ticketing history, or your product catalogue is guessing. Platforms built for enterprise use, such as OpenAI’s Frontier, are explicitly designed to ground agents in company data and systems of record so they retain institutional memory across sessions rather than starting from zero every time. SAP’s Business AI Platform makes a similar case for a unified data and process foundation, arguing that agents need consistent context across end-to-end workflows, not just a single connected app.

Security and governance controls come second, and they are non-negotiable for anything touching customer or financial data:

  • Identity and access management with a distinct agent identity, not a shared service account.
  • Role-based access control (RBAC) that limits each agent to the systems and actions its task actually requires.
  • Audit logs that record every action an agent takes, with enough detail to reconstruct a decision after the fact.
  • Data residency controls where regulatory scope demands it.

Third, monitoring and lifecycle controls: usage and cost reporting, model versioning so you know which version produced a given output, and a defined process for retiring or updating an agent without breaking downstream dependencies. Google’s Gemini Enterprise pairs its no-code agent designer with exactly this kind of enterprise control layer, on the reasoning that speed of adoption and governance have to ship together, not as an afterthought bolted on after launch.

Fourth is the no-code versus code-first decision. No-code builders lower the barrier for domain teams to prototype agents quickly, which is genuinely valuable during discovery. But they tend to hit a ceiling once requirements involve custom integrations, complex conditional logic, or tight performance constraints, at which point code-first development gives you the control and maintainability that a production system needs over its lifetime.

Pro Tip: Ask any vendor or internal team one blunt question before you approve a pilot: “Show me the audit log for an action this agent took last week.” If nobody can produce it in under a minute, the platform is not ready for anything consequential.

What business use cases deliver measurable returns?

Five process areas consistently produce the clearest return on an agent pilot, because they combine high transaction volume with well-defined success criteria.

  • Revenue operations: lead enrichment, qualification, and personalised outreach sequencing, measured against lead conversion uplift and time-to-first-contact.
  • Customer support: tiered ticket triage, knowledge-base-grounded resolution, and escalation routing, measured against resolution rate and average handling time.
  • Procurement: quote requests, supplier comparison, and purchase order drafting within approved limits, measured against cycle time and cost per transaction.
  • Market research: continuous competitor and pricing monitoring with automated summary briefings, measured against analyst hours saved per report.
  • Developer productivity: automated code review triage, test generation, and documentation drafting, measured against pull request cycle time.

Industry reporting already shows this playing out at scale. Oracle’s rollout of AI agents for its sales teams is a concrete signal that major vendors see revenue operations as ready for production agent deployment, not an experimental corner case.

Take a generic support example to see the mechanics. A customer emails a billing query. The agent classifies intent, retrieves the account history and relevant policy documents, drafts a resolution, and checks it against a confidence threshold. Above the threshold, it sends the reply and closes the ticket. Below it, it escalates to a human agent with the draft attached for review rather than starting from scratch. The measurable outcome is a shorter average handling time on the tickets the agent resolves outright, and a shorter time-to-resolution even on escalated tickets, because the human starts from a drafted answer instead of a blank page.

The KPI discipline matters as much as the use case selection. A pilot without a predefined success metric, agreed before the agent goes live, almost always produces an ambiguous result that neither justifies scaling nor kills the project cleanly. Decide the number that matters, cycle time, cost per transaction, resolution rate, before the first line of the agent’s logic gets written, not after.

One partner perspective worth noting for agencies specifically: AI tooling combined with agency workflows is increasingly framed as a way to absorb client volume growth without a proportional increase in staff, which mirrors exactly the productivity logic behind the developer and market research use cases above.

What are the risks and how do you govern them?

Four risks recur across almost every agent deployment that runs into trouble, and each has a workable mitigation.

Hallucination remains the most cited concern: an agent stating something false with full confidence, because language models generate plausible text, not verified fact. Grounding the agent in your own systems of record, rather than letting it rely on general model knowledge, reduces this substantially but does not eliminate it, which is why evaluation has to be continuous rather than a one-off test before launch.

Over-permissioning is the quieter, more dangerous risk. An agent given broad access “to be safe” or “to avoid future permission requests” becomes a single point of failure if its credentials are compromised or its logic misfires. Data leakage follows the same logic: an agent that can query customer records for a legitimate task can also, if misconfigured, surface that data somewhere it should not, an external API call, a log file, a response to the wrong user.

Cascading failures are the risk specific to multi-agent systems: one agent’s faulty output becomes another agent’s confident input, and the error compounds silently across the chain until a human notices the final result looks wrong.

The mitigations map directly onto these risks:

  • Apply least-privilege access to every agent identity, scoped to exactly what its task requires, nothing broader.
  • Require human approval for any action above a defined financial, legal, or reputational threshold.
  • Build synthetic test cases that deliberately probe edge cases and adversarial inputs before production launch.
  • Maintain continuous monitoring with alerting on anomalous action patterns, not just uptime.

Governance needs named ownership, not a shared assumption that “IT will handle it.” A practical structure assigns a business owner accountable for the KPI, a technical owner accountable for the agent’s logic and permissions, and a compliance reviewer for anything touching regulated data. AGID’s 2025 assessment of national AI projects makes the same point from a public-sector angle: transparency and auditable decision trails are what sustain trust in an AI system over time, and that logic transfers directly to enterprise deployments where regulators, auditors, or simply your own board will eventually ask how a decision was made.

A minimal policy checklist covers five points: define the agent’s permission scope in writing, set the approval threshold for autonomous action, log every action with enough detail to reconstruct it, schedule periodic re-evaluation against fresh test cases, and define an incident response path, who gets notified, how the agent gets paused, how the error gets traced, if something goes wrong.

How do you run a pilot and scale what works?

A disciplined pilot-to-scale sequence keeps risk contained while giving a genuine use case the chance to prove itself.

  1. Pick one process with a clear, quantifiable KPI. Choose a workflow that is high-volume enough to matter and simple enough to evaluate cleanly, resist the temptation to pilot your most ambitious idea first.
  2. Decide your build path. In-house engineering gives full control and IP ownership but demands the internal capability to maintain it. Extending an existing enterprise platform, such as the connector and governance layers built into Gemini Enterprise, speeds up integration where your systems already fit the vendor’s ecosystem. Engaging an engineering partner suits cases needing deep custom integration that neither option covers cleanly, particularly where legacy systems or bespoke workflows are involved.
  3. Set evaluation criteria before launch, not after. Define what “working” looks like numerically, then measure against it weekly during the pilot window rather than waiting for a quarterly review to notice a problem.
  4. Instrument cost and version control from day one. Track token usage, API call volume, and which model version generated each output, so a scaling decision is based on unit economics, not enthusiasm.
  5. Scale only what clears its KPI, and scale it into adjacent processes deliberately, one at a time.

Research and vendor guidance both converge on the same pragmatic pattern: start with a grounded, high-value daily workflow rather than a broad transformation programme. Google Cloud’s own enterprise guidance makes this explicit, recommending agents get embedded into the tools employees already use daily rather than launched as a separate system nobody remembers to open.

Pro Tip: Run your pilot for a fixed window, six to eight weeks is usually enough, with a predetermined go/no-go date. Open-ended pilots rarely get cancelled even when they should be, because nobody wants to be the one who calls time on them.

Comparing quotes across potential implementation partners at the build-decision stage is worth the time it takes; a resource like 5Quotes’ AI development comparison guide lays out the kind of questions worth asking any vendor before you commit budget.

Vicedomini Softworks: engineering-first delivery for agent projects

Most agent projects fail not because the model underperforms, but because integration, governance, and long-term maintenance were treated as afterthoughts. Vicedomini Softworks approaches agent engineering from the opposite direction: architecture and governance are designed in from discovery, not retrofitted after a demo impresses a stakeholder.

The engineering-first delivery model removes the account-manager layer that typically sits between a business and the people actually building its software. Clients work directly with the engineers designing the agent’s context layer, its connectors to systems of record, and its permission model, which shortens the distance between a governance requirement and the code that enforces it.

That matters concretely for agent projects because the riskiest parts, RBAC design, audit logging, data residency, connector reliability, are exactly where a disconnected delivery chain introduces the most friction and the most silent gaps. Vicedomini Softworks builds on a stack suited to exactly this kind of production system:

  • Backend and API development in Java, Jakarta EE, Spring Boot, and Quarkus for durable, enterprise-grade service layers.
  • Modern frontend delivery in Next.js, React, TypeScript, and Angular for the interfaces that give humans visibility into agent behaviour.
  • Cloud-native infrastructure on Red Hat OpenShift with Kubernetes orchestration, containerisation, and Zero Trust security practices.
  • Integration patterns including REST APIs, GraphQL, and message-driven architectures for connecting agents to the CRMs, databases, and legacy systems they need to act on.

That combination reduces integration risk because the same engineers who scope the connectors also maintain them after launch, and it improves auditability because logging and RBAC are built against the same architecture from day one rather than layered on top of someone else’s decisions. Peer-reviewed code, targeted testing, and production monitoring back every engagement, which is the difference between an agent that works in a demo and one that survives six months of real transaction volume.

When should you build custom versus buy a packaged agent platform?

Packaged platforms win on speed. A no-code builder gets a reactive agent live in days and suits standard workflows where the vendor’s connectors already match your stack. Custom engineering wins on control: you own the intellectual property, you are not locked into a vendor’s roadmap for a feature you need next quarter, and total cost of ownership tends to favour bespoke work once you factor in the customisation packaged platforms eventually demand anyway.

Bespoke engineering earns its cost when the use case involves deep integration with legacy systems, workflow logic no template anticipated, or a long-term intent to productise the agent into something you sell, not just something you use internally. Choose the packaged route if you need a bounded, standard workflow live fast. Choose an engineering partner if the workflow is genuinely yours, and you expect to still be running it, and improving it, in three years.

— Pepe F.

Build your first agent with an engineering partner, not a demo

Choosing a no-code builder gets a prototype running fast, but the harder part, connecting it safely to your CRM, your finance systems, and your existing authentication layer, is where most pilots stall. Vicedomini Softworks is the alternative to a generic platform trial for companies that need agenti AI per aziende, wired into real systems of record from the start, not bolted on after the demo impresses everyone and then quietly fails an audit.

Vicedomini Softworks

We work through discovery, collaborative engineering, and long-term production support, three phases where you talk directly to the engineers building your agent’s logic and permission model, not an account manager relaying your questions. Relevant services include software development, AI integrations and automation, systems integration, and ongoing maintenance contracts once the pilot proves out. Review the full range of services and get in touch to scope a pilot against a KPI that matters to your business, before committing budget to a platform you have not stress-tested against your own systems.

Sources

FAQ

What are the best AI agents for businesses?

There is no single best option; the right agent depends on your use case, existing systems, and risk tolerance. Enterprise platforms like OpenAI’s Frontier, Google’s Gemini Enterprise, and SAP’s Business AI Platform each suit different integration needs, while custom-built agents from an engineering partner like Vicedomini Softworks fit cases requiring deep legacy integration or bespoke workflow logic.

How can a company use artificial intelligence in its operations?

Companies typically start with a single high-volume workflow, such as customer support triage or lead enrichment, and deploy an agent grounded in their own CRM or ticketing data to automate it end-to-end. Success depends on pairing the agent with governance controls like RBAC and audit logging, not on the model alone.

Are there training courses available for companies adopting AI?

Enterprise platform vendors, including Google Cloud and SAP, publish onboarding and implementation guidance alongside their agent platforms, and sector bodies like AGID publish adoption and governance reports relevant to structured rollout planning. Beyond vendor documentation, most companies pair a pilot project with hands-on training delivered by their implementation partner.

What are the four types of AI?

In agent design specifically, the four common architectures are reactive agents, multi-step planners, multi-agent orchestrated systems, and human-in-the-loop agents, ranked roughly by rising complexity and required oversight. This differs from the older academic classification of AI systems by cognitive capability, which is a separate framework not typically used in enterprise agent planning.