Best fit
Early AI delivery, workflow discovery, prototype hardening, and customer-context translation where business and technical teams need a governed path.
Forward-Deployed AI
A governed operating model for forward-deployed AI delivery.
Forward-Deployed AI is becoming a mainstream enterprise delivery pattern for moving from AI ideas to working systems. Scaled Agents frames that work as a customer-managed, provider-agnostic capability with clear ownership, controls, readiness review, registry entry, evidence, lifecycle visibility, and reusable delivery patterns.
Context
Use this page when delivery work needs to become repeatable governance evidence.
Forward-Deployed AI connects business discovery, workflow design, MVP preparation, readiness review, registry handoff, and reusable patterns. It is meant for teams that need customer-context learning without losing control of scope, ownership, evidence, review paths, and provider-neutral operating posture.
Governance focus
Clarify owner, intended outcome, data boundary, tool boundary, review path, evidence needs, and lifecycle handoff before recurring use expands.
Boundary
This is readiness and delivery planning. It does not approve production deployment, live integrations, customer data access, or autonomous action.
Market Context
FDE is moving from staffing pattern to governed operating model.
Major AI providers and cloud platforms are investing in forward-deployed delivery models because enterprises need more than model access. They need proximity to workflow, production constraints, data boundaries, security review, and repeatable delivery methods. Scaled Agents supports the governance layer around that pattern: customer-managed ownership, Passport-ready scope, Toll Gate review paths, evidence capture, reusable playbooks, and human accountability.
Delivery proximity
Forward-deployed teams can learn from real workflows, but proximity needs boundaries for data, tools, owners, and escalation.
Governed reuse
Each engagement should produce reviewed patterns, not unmanaged shortcuts. Reusable templates need status, assumptions, evidence, and owner review.
Customer control
The durable outcome is not dependence on one provider. It is a customer-managed operating model that can govern AI work across selected providers, clouds, tools, and teams.
SLM Readiness
FDE knowledge should be evaluated before it becomes model behavior.
The FDE SLM Readiness Pack is a candidate offering lane for teams that want to evaluate whether forward-deployed AI delivery knowledge is structured enough for governed retrieval, future model adaptation, or customer-managed SLM support. It starts with source registers, corpus boundaries, golden evaluation cases, refusal rules, and output checks before any model is selected or trained.
Corpus boundary
Separate public-safe FDE operating-model material, internal research, draft playbooks, market signals, and customer-specific context before retrieval or training is considered.
Evaluation harness
Test whether an SLM candidate can classify delivery stages, find missing owners or evidence, map Toll Gate checkpoints, and refuse unsupported provider, legal, compliance, security, or production claims.
Customer-managed path
Keep the model decision behind owner, legal, privacy, security, commercial, RAG, and training review so any future SLM support remains provider-neutral and customer-controlled.
Readiness pack status: planning and evaluation only. Scaled Agents has not released, trained, fine-tuned, deployed, or licensed a Forward-Deployed AI SLM. RAG approval, training approval, model selection, customer-facing release, and commercial packaging require separate owner review.
Operating Flow
From clarity to reusable pattern.
The model keeps AI delivery connected to ownership, review, control boundaries, and operating evidence before AI-supported work becomes consequential action.
Clarity
Define the business problem, workflow context, owner, and intended outcome.
Candidate
Identify where an AI worker or AI-assisted service may support the workflow.
Controls
Set authority limits, data boundaries, review points, escalation paths, and evidence needs.
MVP
Prepare a narrow, reviewable first version before expanding scope or operational reliance.
Readiness
Review ownership, risks, training needs, supporting evidence, and professional-review dependencies.
Registry
Capture status, scope, owner, lifecycle state, and review posture in a governed record.
Lifecycle
Track changes, exceptions, approvals, monitoring needs, and retirement triggers over time.
Reusable Pattern
Feed lessons into future templates, controls, training paths, and operating playbooks.
Pattern Comparison
How Forward-Deployed AI becomes governed capability.
Forward-deployed work is valuable when it bridges customer context and technical execution. The Scaled Agents model adds the governance structure needed to make that learning repeatable, accountable, provider-neutral, and ready for customer-managed operation.
| Forward-Deployed Engineering Pattern | Scaled Agents Model |
|---|---|
| Embeds close to customer problems | Clarifies business problems, workflows, owners, and constraints before AI is built |
| Turns ideas into working systems | Converts use cases into AI worker templates, workflows, and implementation paths |
| Bridges business and technical teams | Connects business owners, technical teams, governance, security, and operations |
| Builds reusable implementation patterns | Creates reusable templates, passports, registries, controls, and delivery playbooks |
| Moves pilots toward production | Supports readiness reviews, approvals, testing, evals, and deployment controls |
| Learns from each engagement | Feeds lessons into the backlog, approval queue, and future platform improvements |
| Scales delivery methodology | Preserves reusable playbooks, governed templates, Passport-ready scope, Toll Gate checkpoints, and evidence patterns |
Governed Delivery
What the model protects.
The operating model is designed to keep AI delivery useful without letting experimentation blur ownership, risk, or control boundaries.
Human accountability
AI workers may assist with permitted work, but people remain accountable for judgment, approval, risk, and outcomes.
Control boundaries
Use cases, tools, data, authority limits, and escalation paths are clarified before broader operating use.
Reusable evidence
Each engagement should produce knowledge that improves future templates, readiness reviews, training, and governance records.
Public review boundary. This page is a high-level operating-model overview. It does not provide legal advice, compliance readiness statement, security conclusion, audit opinion, production use, or customer risk acceptance. Customer-specific outputs require verification and appropriate professional review before reliance or use.