An Agentic AI Solution Provider, Not Just a Vendor
Tools vendors ship licenses; Xyner ships outcomes. As an agentic AI solution provider we bring the platform, configure the agents, integrate your systems, stand up the governance and stay accountable through production — the full distance between a promising demo and a workflow that runs itself.
What is an Agentic AI Solution Provider?
An agentic AI solution provider is accountable for a working result: AI agents that complete a defined business workflow in your systems, under governance your risk team signed off, measured against a metric you chose. The provider supplies the platform, the configuration, the integration work, the governance setup and the production support. If any of those pieces is missing, you have bought a tool and inherited a project.
The distinction matters because agentic AI fails at the seams. A capable platform configured badly stalls; a strong delivery team on thin technology plateaus; and when platform and delivery belong to different parties, each seam becomes a negotiation. Xyner closes the seams by owning both: the agentic AI platform and the delivery organization that implements it, so one party answers for the outcome.
This page gives you the material to evaluate any provider — including us: what separates providers from tools vendors, an eight-point evaluation checklist, the engagement model phase by phase, and the PoC-to-production path with its exit criteria.
Provider versus tools vendor
Six differences that surface within the first month of a real program.
Outcome ownership
A tools vendor's obligation ends at access credentials. A provider signs up for the workflow metric — cycle time, completion rate, cost per case — and structures the engagement so that number is measured, reported and owned.
Implementation capability
Someone must configure agents, map processes and run testing with your teams. Providers bring that capacity and a documented method; vendors leave you to hire it, rent it, or improvise it.
Integration depth
The hard mile is inside your ERP, CRM and ITSM. Xyner arrives with 1,000+ governed connectors and the delivery experience to wire them under scoped credentials — not a list of APIs and best wishes.
Governance setup
Controls only count when configured: who approves what, which action budgets apply, what the audit trail must show. A provider sits with your risk team and builds that configuration before go-live, not after an incident.
Production support
Agents act in systems of record, so support is operational, not a ticket queue: a 99.95% platform SLA, monitoring your operators run, and versioned releases when workflows change.
Transfer of capability
A good provider makes itself progressively less necessary: your builders co-configure, your admins own the controls, and your second use case needs a fraction of the first one's support. Dependence is a vendor model, not a provider model.
The evaluation checklist
Eight criteria to demand from any agentic AI solution provider — with how Xyner answers each.
Platform depth
Verify hands-on, not in slides. Xyner: 250+ agentic capabilities you can exercise in a PoC.
Integration coverage
Map against your estate. Xyner: 1,000+ connectors — SAP, Oracle, Microsoft, Salesforce, ServiceNow, Workday, APIs.
LLM independence
No single-model dependence. Xyner: 12+ families routed by policy, private models included.
Governance & HITL
Ask to trigger an approval live. Xyner: RBAC, approval gates, action budgets, full audit — demonstrable.
Certifications
Documents, not logos. Xyner: SOC 2 Type II, ISO 27001, GDPR-compliant, HIPAA-aligned.
Deployment flexibility
Your posture, not theirs. Xyner: SaaS, VPC, hybrid, on-premise Kubernetes, sovereign, air-gapped.
Delivery methodology
Phases, outputs, exit criteria in writing. Xyner: the six-phase engagement model below.
Post-go-live support
Operations, not a mailbox. Xyner: 99.95% SLA, monitoring consoles, versioned releases.
The engagement model
Six phases, each with an output you can inspect before the next begins.
1. Discovery
Workflows, systems and constraints mapped; candidates ranked by volume, rules and pain.
2. Scoping
One workflow bounded; success metric, data classes and approval model agreed in writing.
3. Proof of concept
Agents on real cases with governance live — the exit criteria decided before it starts.
4. Production build
Full integration, hardening, evaluation suites and user testing with the process owners.
5. Go-live
Deployment to your posture with rollback ready; hypercare while volumes ramp.
6. Run & improve
Your team operates; Xyner supports under SLA; metrics reviewed on a fixed rhythm.
Solutions we deliver as a provider
Problem → agent action → outcome, across the departments where engagements usually start.
Customer service
Routine cases swamp the team → agents resolve them inside the service platform, escalating with context → queues shrink and stay shrunk.
Finance operations
Matching and reconciliation stretch every close → agents match, post within tolerance, escalate breaks → the close lands earlier each month.
Human resources
Onboarding crawls across systems → agents provision, schedule and chase approvals → new joiners work on day one.
IT service management
Access requests dominate the desk → agents fulfil standard requests under approval rules → engineers return to projects.
Procurement
Match exceptions stall supplier payments → agents reconcile documents and chase the gaps → payment cycles become predictable.
Supply chain
Exceptions surface too late to fix cheaply → agents monitor milestones and alert early → recovery happens inside the window.
Compliance
Evidence gathering eats review capacity → agents collect, pre-assess and package for sign-off → reviews finish on schedule.
Sales operations
CRM data rots between quarters → agents maintain records and assemble quotes → forecasts worth presenting.
Shared services
Cross-entity requests ping-pong between teams → agents run the checklists end to end → one accountable flow, fully audited.
PoC to production, step by step
The ten-step path — with the exit criteria that keep a pilot from becoming a permanent pilot.
1. Define success numerically
The metric, its current baseline and the target — agreed before configuration starts.
2. Bound the data
Which data classes the PoC may touch, and which it may not — signed off by risk.
3. Configure the agents
Goals, escalation rules and knowledge sources — your builders in the room from day one.
4. Integrate two or three systems
Only what the workflow needs, under scoped credentials — enough to be real, small enough to be fast.
5. Apply production governance
RBAC, approvals and budgets exactly as production would run them — the PoC proves the control model too.
6. Run real volume
Live or shadow cases at meaningful scale — synthetic demos prove nothing you can take to a steering committee.
7. Measure against the baseline
The step-one metric, tracked by the platform's own monitoring — no spreadsheet theater.
8. Harden for production
Evaluation suites, edge cases, failure drills and operational runbooks.
9. Deploy to your posture
SaaS, VPC, hybrid, on-premise or air-gapped — the proven agents move unchanged.
10. Scale with less of us
Your team runs the second use case with Xyner in support — by the third, you lead.
The integration muscle behind delivery
Solutions land only if they reach your systems. We arrive with the connectors already built.
ERP & finance
SAP, Oracle and finance systems — the heart of most first engagements.
CRM & service
Salesforce, ServiceNow and service desks — where service solutions live.
HR & workforce
Workday, HRMS and payroll — onboarding and requests end to end.
Data platforms
Databases, warehouses and document repositories — evidence with lineage.
Email & collaboration
Mailboxes, chat and calendars — intake, approvals and updates.
APIs & custom systems
REST and SOAP for the systems only you run — scoped and audited.
Every call — pre-built connector or custom API — runs through the same governed execution layer: permissioned, logged, replayable. How agents coordinate across these systems is covered under multi-agent orchestration.
Governance the provider sets up with you
We configure the controls; you own the policy. The split is explicit.
RBAC
Role model designed with your IAM team, enforced by the platform.
Approval gates
Your approvers, your thresholds — wired in before the first live case.
Audit trails
Configured to produce the evidence your auditors actually ask for.
Action budgets
Caps tuned to each workflow's risk, reviewed with your process owners.
Model routing
Data-sensitivity rules deciding which models see which classes.
Data boundaries
Residency and isolation matched to your classification scheme.
Certified foundation
SOC 2 Type II, ISO 27001, GDPR-compliant, HIPAA-aligned.
Evaluation gates
Every change re-tested before production — ours and yours alike.
Deployment options a provider must support
If a provider dictates the posture, the provider is the constraint.
SaaS & dedicated VPC
Fastest delivery under the 99.95% SLA, or VPC isolation where tenancy matters. Posture detail lives on our deployment page.
Managed control plane, your data plane
We keep the platform current; execution and data stay in your environment.
On-premise, sovereign & air-gapped
Kubernetes in your data center or disconnected enclave, private models included.
Provider-delivered agents versus the alternatives
The comparison that matters when someone proposes "just buy a tool" or "just use a chatbot".
| Dimension | Traditional RPA | GenAI assistant | Agentic AI with Xyner |
|---|---|---|---|
| Who is accountable | You, post-handover | No one, for outcomes | The provider, to a written metric |
| Delivered unit | Scripts to maintain | A license to distribute | A governed workflow in production |
| Integration work | Surface-level recording | Left to you | Done by the provider, 1,000+ connectors |
| Governance setup | An afterthought | Rarely exists | Configured with your risk team pre-go-live |
| Exception handling | Bots stop, humans queue | All human, always | Policy-bounded resolution plus escalation |
| Knowledge transfer | Not in scope | Not applicable | Built into every phase of delivery |
| Production support | Ticket queues | Status pages | 99.95% SLA with operational monitoring |
| Scaling pattern | Each bot a new project | N/A | Each use case cheaper than the last |
Frequently asked questions — choosing a solution provider
The questions buyers comparing providers ask us most.
What is an agentic AI solution provider?
An agentic AI solution provider delivers working, governed agent solutions — not just software. The provider brings the platform, configures agents for your workflows, integrates your systems, sets up governance with your risk team and supports the solution in production. Accountability covers the outcome, not only the license.
How is a solution provider different from a software vendor or a consultancy?
A software vendor hands you a product and wishes you luck; a consultancy brings people but rents its technology from someone else. A solution provider like Xyner owns both sides: the platform the agents run on and the delivery capability to put them into production — one party accountable end to end.
What should an evaluation checklist for a provider include?
At minimum: platform depth you can verify hands-on, integration coverage for your systems, LLM independence, governance and human-in-the-loop controls, security certifications, deployment flexibility, a documented delivery methodology, and defined post-go-live support. Score each with evidence, not slideware.
How does an engagement with Xyner start?
With discovery and scoping: your candidate workflows are ranked, one is selected, and a success metric is agreed in writing. A bounded proof of concept follows, designed so your risk team can approve it quickly — real volume, bounded data, bounded actions.
What does a proof of concept look like?
A scoped slice of a real workflow: agents configured for your policies, connected to two or three of your systems, governed with RBAC, approvals and action budgets, and run against real cases with the metric tracked. PoCs prove the governance model as much as the outcome.
Who owns the agents after go-live?
You do. Agents, configurations, integrations and audit history live in your platform tenancy or installation. Xyner supports the platform itself under a 99.95% availability SLA, and your team operates the agents through the monitoring and admin consoles.
How is knowledge transferred to our team?
Deliberately, across the engagement: your builders co-configure agents during delivery, your operators run the monitoring console before go-live, and your admins own RBAC and budgets from day one. The goal is that your second use case needs far less of Xyner than your first.
How are provider engagements structured commercially?
Typically as platform subscription plus scoped delivery for the first use case, with later use cases needing progressively less delivery effort. Exact structure follows your deployment model and scope — Xyner's team works through it during scoping rather than quoting blind.
How are governance responsibilities split between us and the provider?
The platform enforces the controls — RBAC, approval gates, action budgets, audit — and Xyner configures them with you during delivery. Your organization owns the policy decisions: who approves what, which thresholds apply, which data classes reach which models. The split is explicit and documented.
How does Xyner integrate with our existing systems?
Through 1,000+ pre-built integrations — SAP, Oracle, Microsoft, Salesforce, ServiceNow, Workday — plus databases, document repositories and REST/SOAP APIs, all through a governed execution layer where every call is permissioned and logged.
Which certifications should we require from a provider?
SOC 2 Type II and ISO 27001 at minimum, plus GDPR compliance and HIPAA alignment where your data demands it. Xyner holds all of these — and a provider that hesitates to show certificates has answered your question.
What are red flags when choosing an agentic AI solution provider?
Demos that never touch your systems, governance described but never shown, no documented path from PoC to production, dependence on a single LLM vendor, and reluctance to agree a measurable success metric. Any one of these predicts a stalled program.
Compare and decide
The adjacent pages evaluators read alongside this one.
Put an Agentic AI Solution Provider to the test
Bring the evaluation checklist above to a working session. We will answer every line with the platform running.
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