Enterprise AI agents,
on one governed platform.
One agent is a project. Twenty agents are a platform problem. Without shared identity, tools, evaluation, and cost control, every team rebuilds the plumbing and security inherits a shadow AI estate.
We implement the platform layer on your cloud, then prove it with two working agents. The goal is not more pilots. It is a foundation your teams can build on without asking permission for the basics every time.
What you get
- ✓A platform with two reference agents, not a pile of pilots
- ✓Permissions, approvals, and audit trails by default
- ✓Model and cloud spend you can see and cap
Fixed scope before work begins. Senior delivery, not a handoff.
Quick answer
An enterprise AI agent platform is the shared environment for building, running, and governing multiple AI agents. Spurto implements the runtime, model gateway, tool connectors, identity and permissions, retrieval, evaluation, observability, and cost controls on Vertex AI, Azure AI Foundry, or AWS Bedrock. Engagements typically run 10–20 weeks.
When you need a platform, not another agent
Multiple teams are building agents on different stacks and keys.
No one has a reliable inventory of which agents exist or what they can access.
Agents use shared API keys and broad data access.
There is no evaluation gate before an agent reaches production.
Model spend is unpredictable and unattributed.
Security review starts from scratch for every new agent.
What the platform layer includes
Platform strategy
Use-case inventory, cloud and identity review, build-versus-buy, and a target architecture tied to a business case.
Secure foundation
Landing zone, networking, identity, secrets, model gateway, and logging. The part most pilots skip.
Core services
A typed tool and connector catalogue, retrieval layer, evaluation service, and policy engine shared across agents.
Reference agents
Two or three priority agents migrated or built on the platform, so the foundation is proven rather than theoretical.
Governance
Agent registry, approval workflow, cost budgets, data boundaries, and runbooks. Who owns what, and who can see what.
Enablement
Templates, SDK guidance, documentation, and training so your engineers can ship the next agent without us.
How we implement it
Strategy
Two weeks: use-case inventory, cloud and identity review, build-versus-buy. Output: a platform blueprint and business case.
Foundation
Three to four weeks: landing zone, networking, identity, secrets, model gateway, and logging. Output: a secure platform foundation.
Core services
Three to four weeks: tools, retrieval, evaluation, and policy. Output: shared services live for the first agent teams.
Reference agents
Three to four weeks: migrate or build two to three priority agents. Output: working reference implementations.
Govern and enable
Two to four weeks: registry, approvals, budgets, documentation, and training. Output: an operating model your team can run.
What you walk away with
| Deliverable | Format | Decision it enables |
|---|---|---|
| Platform blueprint | Architecture + business case | What to build and why |
| Secure foundation | Landing zone, identity, secrets | Where agents run |
| Model gateway | Routing, fallback, cost tracking | How models are chosen and switched |
| Tool catalogue | Typed connectors + permission scopes | What each agent may touch |
| Evaluation harness | Golden sets + release gate | Whether an agent ships |
| Agent registry | Inventory + ownership model | Who owns what, and who can see what |
Who this is for
Mid-market and enterprise organisations running several AI initiatives
CTOs who need to standardise before shadow AI spreads
Regulated businesses that need central control over models and data
Groups with multiple business units or acquired companies
Companies whose cloud offers an agent platform but lack the internal expertise to implement it
Not for
- —A single AI use case. Start with AI advisory or one production agent.
- —A team with no platform engineering capacity to inherit the environment.
- —An organisation that has not yet proven one agent in production.
Things buyers usually ask
What is an enterprise AI agent platform?
It is the shared infrastructure and governance layer for building, running, and monitoring multiple AI agents. It typically includes an agent runtime, model gateway, tool connectors, identity and permissions, retrieval, evaluation, observability, and policy controls. The platform is infrastructure; the agents are the applications.
Which platform should we choose - Vertex AI, Azure AI Foundry, or AWS Bedrock?
It depends on your existing cloud, identity system, data platform, and regulatory constraints. We assess the three against your environment and recommend the one with the lowest integration and operating burden. We are platform-agnostic and do not resell any of them.
How is this different from ChatGPT or Microsoft Copilot?
ChatGPT and Copilot are end-user products. An enterprise agent platform lets you build custom agents that access your systems, enforce your permissions, call your tools, and produce auditable outcomes. The model may be the same; the control, integration, and accountability are not.
Do we need a platform if we only have one agent?
Probably not. Start with one production agent, prove the value, and revisit the platform question when a second or third team wants to build. Building a platform before you have a working agent is a common and expensive mistake. We will tell you if you are early.
How long before we see value?
The platform foundation is usually complete in six to eight weeks, and the first reference agent can be live within eight to ten weeks. Governance, enablement, and migration of existing pilots follow. Value should be measured on the reference agent, not on the platform's existence.
Can you migrate our existing pilots onto the platform?
Yes. We assess existing agents, standardise their tool access, add evaluation, and migrate them incrementally. We do not recommend a big-bang rewrite. The first migration is chosen to prove the model, not to show how much can be moved at once.
How do you control model cost?
A model gateway tracks routing, tokens, latency, and spend per agent, team, and use case. We set budgets, alerts, rate limits, and fallback rules, and review cost alongside quality. That turns 'AI is expensive' into a managed line item with an owner.
Does the platform support DPDP and regulatory requirements?
We design data boundaries, purpose limitation, access control, retention, audit trails, and human-oversight thresholds into the platform. For NBFCs, decisions must also be explainable and auditable. Where legal interpretation is needed, we work alongside your counsel. The platform logs what each agent accessed and why.
Tell us what you are trying to fix.
We will tell you if we can help.
A 30-minute review with the person who would run the work. No pitch deck, no commitment.