YAA\IF
The cost of run-the-business is climbing
Headcount, vendors, and ticket volume keep growing. The work behind them has not changed in a decade. Boards are asking why AI has not bent that curve yet.
For the office of the CIO and CTO
Run enterprise agents on your terms. Open standards. Real governance. Inside your perimeter.
YAAIF is the enterprise agentic AI OS. An open-standards platform that puts governed agents to work across your business with no vendor lock-in, and with compliance built in from day one. We help CIOs and CTOs move from first pilot to enterprise rollout without giving up control.
- Open standards, no lock-in
- Enterprise-scale by design
- Compliance built in from day one
Why now
Three pressures every executive is already feeling
Ungoverned copilots are creating shadow risk
Point copilots and unmanaged scripts are quietly touching customer data and core systems. Most have no policy, no approvals, and no usable audit trail.
Regulators have stopped waiting
Data residency, model accountability, and the right to explain are moving from policy drafts into enforced obligations across regions and industries.
The decision lens
What you should actually be evaluating
Strategic, financial, and operational fit
- Strategic fit. One governed platform across business functions, instead of another point tool that fragments the AI portfolio.
- Total cost of ownership. Replaces piecemeal RPA, scripts, and copilots with reusable agents and shared governance.
- Vendor risk. Runs inside your perimeter. Configurations and data are yours. No proprietary lock-in to a single model provider.
- Change management. Business users speak to it in natural language. The technical lift sits with the platform team, not with every department.
- Regulatory posture. Per-action approvals and run-level audit logs match the language regulators are already using.
Integration, identity, and exit posture
- Integration surface. Connects to your existing systems of record through your existing APIs and identity; no parallel data lake required.
- Identity and access. Federates with your IdP; agent permissions inherit from the same roles your people already have.
- Observability. Every run is a correlated trace with cost attribution and an append-only audit path. Traces, metrics, and logs flow into the OpenTelemetry, Prometheus, and log stack your platform team already runs.
- Data residency. Choose the cloud region or on-prem cluster; data and secrets never leave your tenant.
- Exit and portability. Open formats for prompts, workflows, and audit data. Your configurations come with you if you ever leave.
How it works
Five layers, one platform
Every request travels the same path through the YAAIF stack: from the channel it arrived on, through the agent that owns it, down to the model that reasons about it. Each layer has its own role, its own governance, and its own audit trail.
-
L1
Channels
Where work comes in
Microsoft Teams, web ChatUI, Claude Desktop, ChatGPT, email, voice, APIs, event streams, and any MCP-compatible client. Channel input is normalized into one contract before it reaches the agent.
-
L2
Agent
Goal-driven orchestration
The agent owns the request. It routes channel input, selects the execution path, and coordinates downstream steps. Policy gates and human approvals sit in line, not bolted on afterwards.
-
L3
Skills
Playbooks and routing
Skills map intent to reusable capabilities. They decide which tools should run for the current request and in what order, with the same playbooks reused across agents and channels.
-
L4
Capabilities
Execution against your systems
The execution layer invokes MCP tools and your existing integrations to do the actual work: writes to systems of record, queries against your data, triggers in downstream workflows.
-
L5
LLM Models
The reasoning engine
Any commercial provider or a self-hosted open model, hot-swappable per agent and per skill. Model choice is a platform decision, not a vendor lock-in.
Three patterns, one platform
Where YAAIF shows up in your operation
Three patterns cover the shape of nearly every operational workflow. All three share the same governance posture. The difference is how the agent reaches the work, not whether the work is controlled.
Pattern 01
Chat Agent
Users ask in natural language. The agent switches to structured input when precision matters.
People describe what they need in the chat tools they already use. The agent reads your context, drafts the work, waits for approval, and then executes against your systems of record. For critical workflows, the agent renders structured input cards inside the chat window: explicit fields, validations, and choices that keep free-text ambiguity out of high-stakes requests.
Replaces: helpdesk overflow, repetitive request handling, slow first response, and misread intent on high-stakes requests.
Pattern 02
Desktop Agent (Computer Use)
The agent takes action on the user's system, on their behalf.
The Desktop Agent operates the applications your team already uses, including legacy applications that never got an API. It clicks, types, fills forms, and moves data between systems on the user's behalf. It runs on the user's own desktop or on a managed virtual desktop pool, and it carries the same approval and audit posture as the Chat Agent. The human stays in control; the agent handles the swivel-chair work.
Replaces: legacy app fragility, manual data entry, and hand-offs between apps.
Pattern 03
Ambient Agent
The agent orchestrates AI, people, and systems across end-to-end business processes.
Always on. The Ambient Agent watches events, schedules, and signals from your systems, then runs a governed end-to-end flow that coordinates AI, people, and systems of record across the full business process. Humans stay on the approval gate for anything that matters, with the trust, governance, and control that mission-critical operations demand.
Replaces: missed SLAs, late detection, after-hours escalations, and manual hand-offs across teams and tools.
Knowledge of your business is a capability inside all three patterns. It is not a fourth product to buy. The agent reads your context (documents, tickets, runbooks, history) wherever the work needs it.
Channels
Where work comes in
Same governance, same audit, same approval posture, regardless of how the request arrived. Channels are abstracted behind a common ingress contract, so adding a new one is a configuration change rather than a re-platforming exercise.
Chat & collaboration
Microsoft Teams, Slack, and Google Chat, plus web chat and embedded widgets inside your own internal applications. The tools your people already use.
Email & ticket queues
Shared mailboxes, helpdesk inboxes, and service management queues (ITSM, CRM cases) trigger workflows automatically, under the same policy and approval controls. The original message is preserved in the audit trail.
Voice & telephony
Contact center and IVR integration for agent-assisted call handling and post-call automation. The call transcript becomes structured input to the same workflows every other channel feeds.
APIs & webhooks
Programmatic invocation from your existing applications, middleware, and partner systems. Standard REST and webhook patterns, authenticated through your IdP.
Event streams
Kafka, AWS EventBridge, Azure Service Bus, MQ, and webhook firehoses. Always-on triggers feed the Ambient Agent pattern, catching events before they become escalations.
Schedules & timers
Cron expressions, business calendars, and SLA timers for recurring governed work, end-of-period reconciliations, and proactive checks that run on their own.
Plus
MCP Hosts: bring YAAIF into the AI clients your people already use
YAAIF exposes its governed skills and tools through the open Model Context Protocol (MCP). External AI clients can invoke YAAIF workflows directly, under the same approval gates, policy enforcement, and audit trail as every other channel.
Out of the box
Claude Desktop
One-click connect via standards-based OAuth (PKCE). Active sessions are tracked per user, and admins can disable a host with a single switch in the Manage Agents console.
Out of the box
ChatGPT desktop app
Same MCP host flow as Claude Desktop. Matching session controls, audit events, and approval gates, so users keep their preferred client without losing governance.
Open protocol
Any MCP-compatible client
Custom internal copilots, IDE plugins, and partner clients connect through the same protocol. No bespoke integration work per host.
The benefit: power users keep their preferred AI client. The platform keeps governance, approvals, and audit.
The channel is configurable. Governance is not. The same policy gates, approvals, and audit trail apply to every request: a chat message in Teams, an inbound email, a partner webhook, an overnight Kafka event, or a user typing into Claude Desktop.
Deployment
Run YAAIF wherever it fits your operating model
Same platform, same governance, same audit trail. You decide where the stack lives: on your own bare-metal Linux, inside your cloud tenant, or as a managed service we operate for you.
Option 01
Bare-metal Linux
Deploy directly onto your own Linux hosts, including air-gapped and sovereign environments. No cloud dependency, no call-home, and no shared multi-tenant components. Fits into the operations runbook your platform team already runs.
- Runs on standard x86_64 or ARM Linux distributions
- Works air-gapped, with sealed update bundles
- Integrates with your existing identity, secrets, and SRE tooling
Option 02
Your cloud, your tenant
Deploy into your AWS, Azure, GCP, or any Kubernetes-capable private cloud. The platform lives in your accounts, your VPC or VNet, your region, under your network and IAM controls. Spend stays on your existing cloud commitments.
- Customer-managed AWS, Azure, GCP, or other Kubernetes
- Your IAM, your KMS, your network policies
- Regional pinning for data residency and latency
Option 03
Managed SaaS by us
Prefer to skip the platform engineering? We operate a dedicated YAAIF environment for you on our infrastructure. Same governance, same audit posture, same controls. We handle the upgrades, the patching, and the on-call rotation.
- Dedicated single-tenant environment per customer
- We operate the platform, upgrades, and SRE
- Fastest path to first pilot and first production rollout
The choice is reversible. Customers often start on managed SaaS for the first pilot, then move into their own cloud tenant or bare-metal cluster as governance maturity catches up. Open formats for configurations, workflows, and audit data keep the migration straightforward.
How YAAIF compares
Where it fits next to the AI tools you already have
Most enterprises already pilot Microsoft 365 Copilot, Claude Enterprise, ChatGPT Enterprise, SAP Joule, or Salesforce Agentforce. Copilot, Claude, and ChatGPT are strong productivity assistants. Joule and Agentforce embed AI inside SAP and Salesforce. YAAIF answers a different question: how do you run governed, multi-step work across enterprise systems, inside your own perimeter, with approvals, policy, and audit on every action?
| Capability | YAAIF | Microsoft 365 Copilot | Claude Enterprise | ChatGPT Enterprise | SAP Joule | Salesforce Agentforce |
|---|---|---|---|---|---|---|
| Primary purpose | Governed multi-step agent workflows across enterprise systems | Productivity copilot inside Microsoft 365 apps | Conversational AI for knowledge work and shared Projects | Org-wide conversational AI with GPTs and connectors | AI copilot and agents across SAP applications (finance, supply chain, HR) | AI agents for CRM, service, sales, and marketing inside Salesforce |
| Deployment model | Your bare-metal Linux, your cloud (AWS, Azure, GCP, Kubernetes), or managed SaaS by us | Vendor SaaS on the Microsoft cloud | Vendor SaaS (Anthropic; optional AWS Bedrock routing) | Vendor SaaS on OpenAI infrastructure | SAP-managed cloud on Business AI Platform (Joule Studio runtime) | Salesforce multi-tenant cloud (Hyperforce regions) |
| Where your data sits | Inside your perimeter; never leaves your cloud or data center | Your M365 tenant plus Microsoft processing boundary | Anthropic infrastructure under enterprise terms | OpenAI infrastructure under enterprise terms | SAP Business AI Platform and SAP cloud processing boundary | Salesforce org and Data 360 under enterprise terms |
| Model choice | Model-agnostic: any commercial provider or a self-hosted open model | OpenAI models routed by Microsoft | Anthropic Claude family only | OpenAI GPT family only | Partner LLMs routed through SAP Business AI Platform | Models hosted via Salesforce Atlas and Einstein Trust Layer |
| Governance posture | Per-action approvals, policy gates, and role boundaries on every run | Tenant admin, Purview DLP, sensitivity labels | Workspace admin, SSO, usage policies | Workspace admin, SSO, DLP integrations | Runtime guardrails, AI Agent Hub, identity-bound execution | Agentforce 360, Data 360 access controls, org guardrails |
| Audit trail | Append-only structured trace per run, kept separate from telemetry | Activity logs via Purview and Defender | Admin audit logs in console | Compliance API and admin logs | Runtime audit logging and Agent Hub lifecycle monitoring | Agent Platform Tracing and session logs in Data 360 |
| Multi-step workflows | Native multi-agent orchestration across Chat, Desktop(Computer Use), and Ambient Agents, with approvals built in | Copilot Studio (low-code) and agents in M365 | Computer use and Projects (evolving) | Agents, Tasks, and Operator (evolving) | Joule Studio agents and workflows across SAP processes | Native multi-agent orchestration (Supervisor pattern) in org |
| Integration surface | Your existing APIs, IdP, and SRE stack. No parallel data lake required. | Microsoft Graph and Power Platform connectors | MCP, APIs, Projects context | Connectors, GPTs, and Actions | S/4HANA, SuccessFactors, Ariba, and SAP Integration Suite | Salesforce CRM, Data 360, MuleSoft MCP connectors |
| Ownership of configurations | You own configs, prompts, workflows, and audit history | Workflows live inside the Microsoft platform | Projects live inside the Anthropic platform | GPTs live inside the OpenAI platform | Agents and workflows live inside SAP Business AI Platform | Agent configs, topics, and actions live in Salesforce org |
| Commercial model | Platform plus usage; no per-seat lock-in | Per-seat add-on to M365 | Per-seat plus usage | Per-seat enterprise plan | SAP subscription and capacity licensing | Salesforce org licensing plus Agentforce consumption |
Where YAAIF is the right call
- Work must happen inside your perimeter for regulatory, sovereignty, or IP reasons.
- You need governance on actions, not only on chat content.
- The workflow crosses multiple systems of record, rather than a single document, mailbox, or chat thread.
- You want optionality across model providers, not one vendor's roadmap.
- You expect an auditable trace per action, not only per session.
Where to keep using the others
- Personal productivity inside Word, Outlook, Excel, and Teams. Microsoft 365 Copilot is well-placed.
- Open-ended individual research, drafting, and analysis. Claude Enterprise and ChatGPT Enterprise are strong fits.
- SAP-centric finance, supply chain, and HR workflows inside your SAP landscape. SAP Joule is the natural fit.
- CRM, service, and revenue workflows inside Salesforce. Agentforce is built for that stack.
- Lightweight team-level GPTs or Projects for narrow knowledge tasks. Use the vendor that already has your seats.
- YAAIF is the governed platform layer underneath cross-system work. It complements desktop and ERP/CRM AI; it does not replace them.
Comparison reflects published vendor positioning as of mid-2026. Vendor capabilities evolve quickly. YAAIF sits at a different layer: an enterprise agentic AI OS, not another copilot or ERP/CRM add-on.
Proof
The outcomes executives actually report
Outcome ranges from active programs. We share customer-specific detail under NDA on a briefing call.
40–70%
Reduction in repetitive ticket volume on automated queues
2–5x
Faster time-to-resolution on covered workflows
100%
Of agent actions captured in a regulator-ready audit trail
Boardroom scenarios
Operations: SLA stabilization
Before: Priority incidents waited in queues while analysts hunted for context across tools.
After: Routine triage and routing run automatically; senior analysts handle only exceptions and policy approvals.
Finance: exception follow-through
Before: Reconciliation exceptions sat across spreadsheets, email threads, and ERP screens.
After: A single governed workflow gathers context, proposes the fix, and waits for the controller to approve.
Risk: audit-ready automation
Before: Automation was blocked because no one could explain who did what, when, and why.
After: Every action carries an approver, a policy reference, and a structured trace ready for regulators.
Trust
Control that holds up to internal audit and regulators
Governance by default
Policies, role boundaries, and approval gates apply to every agent, every action, every time.
Federated identity
Plugs into your IdP. Agents inherit the same access boundaries as the humans they assist.
Run-level audit
Every prompt, decision, approval, and action is captured as a structured, queryable trace. See the observability section for the full evidence layer.
Data residency
Choose your region, your cluster, your cloud. Your data does not leave your perimeter.
Your IP, your configurations
Workflows, prompts, and policies you create belong to you, not to the platform vendor.
Change control
Versioned configurations and environment promotion fit your existing release process.
Observability
The Third Eye: every request correlated across seven layers
Trust is the promise. The Third Eye is the evidence. Every request, whether it arrived from a Teams channel, a portal, or an API, is correlated end-to-end across seven layers (Channel, Agent, Skill, Tool, Model, Memory, Response) and reconstructable on a Monday morning when someone asks what the agents actually did over the weekend.
YAAIF Third Eye
Request R1 · 158cd785…6dfa
tokensin 703·out 33·total 736
- L1 Channel Teams inbound transport · 1 req · 24 events
- L2 Agent Default Agent orchestration · 2 turns · healthy path
- L3 Skill equipment-details-read capability routing · 1 selected · 5 events
- L4 Tool sap_get_equipment_data execution · 1 started · 2 I/O captured
- L5 Model openai / gpt-5.4 LLM context · 2 calls · 2 contexts · 1 patch
- L6 Memory Episodic agent memory · 3 events · 3 active · 0 evicted
- L7 Response Completion response stage · 1 completed · 0 failures
flow
intake
LLM turn 1
tool loop · skill_search
LLM turn 2
response · completion
Representative Third Eye view. Select any request to highlight its path across every layer. Switch between Flow (chronological reasoning) and Layers (system correlation) without leaving the trace.
Request overlay across 7 layers
One correlated trace per request: who asked, which agent orchestrated it, which skill was selected, every tool called, every model invocation, every memory event, every response. Re-construct what happened without piecing logs together.
Cost transparency by user, agent, and workflow
Tokens, model spend, and credits are attributed to the tenant, user, and workflow that incurred them. No surprise invoices and no untracked AI spend.
Audit kept separate from telemetry
Operational logs are for debugging and stay fail-open. Approvals, policy decisions, and external-system access keep their own append-only audit trails for regulators and internal review.
Drops into your SRE stack
Built on open standards (OpenTelemetry traces, Prometheus metrics, standard log shipping) so traces, metrics, and logs flow into the dashboards and alerting your platform team already runs.
Session → request → turn → tool loop
The Third Eye preserves the real hierarchy: a session holds many requests, each request its turns, each turn its tool loops and model calls. Streaming progress for in-flight work, durable history for the questions that come weeks later.
Tenant-scoped from ingestion to query
Multi-tenant isolation enforced at the data layer, not just the UI. One tenant's runs, sessions, and costs never surface in another's views or alerts.
Engagement model
A 30-day pilot framed as a decision milestone
The point of the pilot is not a demo. It is a defensible answer to one question: should we standardize on this for the enterprise?
Align
Pick the workflow, agree on success metrics, and stand up the environment inside your perimeter.
Prove
Run the workflow end-to-end with approvals on; measure throughput, exceptions, and analyst time saved.
Measure
Present hard numbers, regulator-ready audit samples, and a clear go / no-go for enterprise rollout.
Use cases
Where YAAIF runs inside the enterprise
Three lenses for finding the workflow that matters this quarter: by business domain, by industry vertical, and by enterprise system of record.
Finance close on SAP S/4HANA, KYC refresh in Financial Services Cloud, prior authorization on Epic, FNOL through claims triage in Guidewire, order-to-activate fallout across OSS/BSS, and dozens more shapes of governed cross-system work.
FAQ
The questions executives actually ask
Will our data train someone else's model?
No. YAAIF runs inside your tenant. Your prompts, documents, and execution traces stay within your control boundary and are never used to train shared models.
What happens to our data if we stop using YAAIF?
Because YAAIF runs in your cloud or on-prem environment, the data, configurations, and audit history remain in your infrastructure. Your data is not held by a vendor you can no longer leave.
Who owns the agents and workflows we configure?
You do. Configurations, prompts, and workflow definitions you create are your intellectual property. YAAIF provides the platform; you own what runs on it.
How is YAAIF different from a copilot or AI assistant?
Copilots help one user at a time. YAAIF runs governed, multi-step work across your enterprise systems with approvals, policy enforcement, and an audit trail per execution.
What does a pilot actually cost in our team's time?
A typical 30-day pilot needs an executive sponsor, a process owner who can dedicate a few hours per week, and a platform engineer for the first week of integration. We run the rest.
How do you handle regulated workflows?
Every agent action passes through configurable policy gates and human approvals before execution. Every decision, input, and outcome is logged for regulators and internal audit.
Where can we get the technical brief?
Architecture, deployment, and security detail is available under NDA. Request the technical brief during your briefing call.
Next step
30 minutes with an executive sponsor and a platform engineer
We will review your current AI portfolio, the one workflow that matters most this quarter, and what a defensible 30-day pilot would look like inside your environment.