Platform — Redouble AI · Silverlake, the Java-native enterprise AI platform

Why this platform exists

An Enterprise Agentic System is, Above All,
an Enterprise System

The requirements that have governed enterprise software for decades remain in effect when AI enters the building:

Identity systems that govern access

Databases that enforce integrity

Audit trails that satisfy regulators

Observability stacks that surface failures

They apply to the agent the same way they apply to the batch job, the form handler, and the legacy application from the 1980s. New capabilities arrive inside that reality, on the enterprise's terms.

At a glance

Silverlake Maps Agentic Concerns onto Enterprise Primitives

What agentic systems add is a new layer of concerns the enterprise playbook has not had to formalize, posing genuine new engineering problems:

Non-deterministic reasoning across long horizons
Outputs that flow between agents with no trusted perimeter
Resource profiles that span orders of magnitude in latency and cost
Actions that have to explain themselves to both humans and other agents

Agent identity is the existing identity layer, extended. Agent audit is the existing audit framework, extended. Orchestration, permissions, rate limiting, observability, fault tolerance: each agentic concern lands on the enterprise primitive that already carries the same shape of problem. An engineer fluent on the enterprise side can now build agentic enterprise software.

Very, very cool enterprise software.

Write an Agent in Java

// Define the agent
class ClaimAgent extends Thinker<ClaimIn, ClaimOut> {
  setObjective("Review claim, verify coverage...");
  registerTool(GetClaimDataTool.class);
  registerGuardrail(ClaimScopeGuard.class);
}

Redouble AI Platform

Multi-Agent Orchestration

Long-running agents
Self-correcting
Human-in-the-loop

Production Infrastructure

Model-agnostic
30x throughput
Rate-limited & scalable

Complete Observability

Every decision logged
Full job lineage
Queryable records

Production AI Workflow

Compliant

SOC2 Type 2 · HIPAA · GDPR · NIST AI RMF · ISO 42001-ready

Security-Hardened

RBAC + ABAC · Enforced scopes · Threat detection & escalation

Fully Auditable

Every AI decision logged with full reasoning chain

Massively Scalable

Thousands of concurrent agents · Self-optimizing hardware & token utilization

Your Infrastructure Choice

On-prem · Private cloud · Air-gapped · Integrates with existing systems

// The agent
class ClaimAgent extends Thinker<ClaimIn, ClaimOut> {

  ClaimAgent() {
    setObjective(
      "Review the hospital indemnity claim. " +
      "Verify eligibility, check coverage, " +
      "validate documentation, calculate payment.");

    registerTool(GetClaimDataTool.class);
    registerTool(CheckCoverageTool.class);
    registerTool(CalculatePaymentTool.class);
    registerGuardrail(ClaimScopeGuard.class);
  }
}

// A tool: thin data accessor
class GetClaimDataTool extends Tool<ClaimId, ClaimData> {

  ClaimData execute(ClaimId in) {
    return db().get(ClaimData.class, in.id());
  }
}

A closer look

Agents and Tools

If the agent is the human in the workflow, the tools are the software that human reaches for. In Silverlake terms, the agent is a Thinker (the Redouble AI terminology for a loop agent): it receives an objective and a set of tools and decides autonomously which to use, in what order, and how to interpret the results.

Each tool does exactly one thing, with scoped permissions and type-safe inputs and outputs. A workflow can run for hours across dozens of agents, and every step is governed.

Agents are tools too: any Thinker can be registered as a tool on another Thinker. Compose arbitrary DAGs of specialized agents (each with its own objective, tools, and guardrails), and the platform handles orchestration.

Integrations

Most of your data already has a Java client. Databases, filesystems, S3, SOAP and REST APIs, message queues, internal services. Whatever your enterprise already runs on, your agents connect to it directly, using the libraries, credentials, and access controls your applications already trust. No rewriting data sources into intermediate protocols to make them agent-reachable.

For third-party AI tools and cross-vendor agent-to-agent flows, the platform speaks MCP (STDIO and HTTP) and A2A natively. Paywalled and quota-gated services are handled with credential management and per-call accounting. Every connector sits under the same guardrail, permission, and observability layers as everything else in the platform.

// Verifies the claim belongs to a project
// the agent is authorized to operate on.
class ClaimScopeGuard extends Guardrail<ClaimId> {

  void validate(ClaimId in) throws GuardrailException {
    Long requested  = db().get(ClaimData.class, in.id()).projectId();
    Long authorized = thinker().scopeId();

    if (!requested.equals(authorized)) {
      throw new GuardrailException(
        "Claim " + in.id() + " is outside scope " + authorized);
    }
  }
}

// Wired into the agent. Runs on every tool invocation.
class ClaimAgent extends Thinker<ClaimIn, ClaimOut> {

  ClaimAgent() {
    registerGuardrail(ClaimScopeGuard.class);
    // ... tools registered here
  }
}

Deterministic Guardrails

When a human uses enterprise software, their permissions are checked and their inputs validated on every call. Agents get the same treatment. Every tool call is validated by guardrails enforced in code: role-based permissions, business logic checks, data scope enforcement. Agent outputs are validated, scoped, and permission-checked before they touch any system. Production-grade rigor, applied consistently across every agent, every tool, every handoff.

// Domain object. Most fields visible to the LLM verbatim.
// One field is summarized on the way out.
class ClaimArtifact extends Artifact {
  Long       id;
  String     memberId;
  LocalDate  dateOfService;
  Money      billedAmount;
  Money      allowedAmount;

  @LLMSummarizable
  String     chartNotes;    // full text in registry; summary to LLM
}

// Tool produces the artifact. Framework wraps it before
// the LLM sees it - chartNotes goes through the summarizer.
class FetchClaimTool extends Tool<ClaimId, FetchClaimOutput> {

  FetchClaimOutput execute(ClaimId in) {
    return new FetchClaimOutput(
      db().get(ClaimArtifact.class, in.id()));
  }
}

Uncorruptible Domain Objects

Domain objects are protected at the architectural level: no data deterioration or permissions creep across agent-to-agent handoffs, regardless of chain length. Long chains mean agents can be highly specialized. Specialized agents are more secure, more repeatable, and more resistant to prompt injection. Narrow, well-defined tasks also fit smaller, cheaper models without loss of quality. Better security, better output, lower cost, all flowing from the same architectural decision.

Resource Management

The platform allocates every resource precisely across concurrent workflows: database connections, LLM tokens, HTTP permits, custom rate limiters. Agents don't hold any of them while idle; each is acquired for the instant it's needed and released immediately after. Failure domains are isolated at the agent level. Virtual-thread concurrency lets standard hardware host thousands of concurrent agents.

Inference Optimization

The biggest cost of running AI agents is inference – and it's the one cost managed platforms are structurally ill-suited to optimize. They operate outside the agent: they see containers and sessions, but they can't see inside the agent's reasoning. The agent code can't reach down to optimize the runtime it's sitting on either. Two separate worlds.

Silverlake unifies both. The platform sees every decision the agent makes, and the agent benefits from every optimization the platform provides. The result is a pipeline of optimizations only possible when runtime and agent share the same process:

Intelligent Compaction

Automatic five-level context compaction that proactively shrinks the prompt while preserving essential information

Field Summarization

Large data objects are automatically condensed before entering the prompt, with full data still accessible on demand

Two-Tier Retrieval

Retrieves broadly, then precisely, so only the most relevant information enters the context window

Incorruptible Domain References

Data flows by reference across agent handoffs, eliminating hallucinated-identifier cascades

Surgical Retry

Failed steps retry in isolation; the rest of the chain stays intact

Pre-execution Guardrails

Bad input caught before the tool runs, before any inference is spent

Observability

Every agent decision emits a structured reasoning trace: what tools were called, what data was retrieved, what the agent considered, what it concluded, and why. Every token tracked. Full job lineage from start to finish. All records structured, queryable, and ready for compliance, debugging, or continuous improvement. Humans and other agents can inspect, verify, and intervene at any point in the flow.

In production

Built for the Most Demanding Enterprise Environments

Compliant

SOC2 Type 2, HIPAA, GDPR, NIST AI RMF and ISO 42001-ready.

Security-Hardened

Independent pen testing, RBAC+ABAC, enforced scopes, threat detection and escalation.

Fully Auditable

Every AI decision with full reasoning chain: structured, queryable, exportable.

Scalable

Thousands of concurrent agents on virtual threads, self-optimizing hardware and token utilization.

Self-Correcting

Agents detect errors through structured validation and correct course without corrupting state.

Model-Agnostic

Frontier or open, commercial or self-hosted. One platform supports them all.

Deployment-Flexible

Private cloud, managed cloud, on-prem, hybrid or even air-gapped, running on AWS, Azure, GCP or anywhere else.

Food for thought

Here are some seed ideas for illustration, all of which our clients and partners across private and public US companies already use our platform for successfully. Silverlake supports any shape you can imagine

Document Analysis at Scale

Ingest hundreds of files and interrogate them with natural language queries. Full source references on every output. Automatic anomaly and red flag detection.

Multi-Agent Research Workflows

Agents that autonomously gather data from multiple sources, analyze it, cross-reference findings, and produce comprehensive reports with full provenance.

End-to-End Process Automation

Claims processing, regulatory document drafting, portfolio monitoring, clinical data validation, due diligence: entire business processes orchestrated by agents, with experts in control at the checkpoints that matter.

Institutional Knowledge Capture

Best practices, methodologies, and expert judgment turned into governed, auditable, reusable tools that emulate how the organization actually works.

AI That Speaks Your Language: Silverlake, the Java-Native Enterprise AI Platform.

An Enterprise Agentic System is, Above All,
an Enterprise System

Silverlake Maps Agentic Concerns onto Enterprise Primitives

What agentic systems add is a new layer of concerns the enterprise playbook has not had to formalize, posing genuine new engineering problems:

Multi-Agent Orchestration

Production Infrastructure

Complete Observability

Compliant

Security-Hardened

Fully Auditable

Massively Scalable

Your Infrastructure Choice

Agents and Tools

Integrations

Deterministic Guardrails

Uncorruptible Domain Objects

Resource Management

Inference Optimization

Intelligent Compaction

Field Summarization

Two-Tier Retrieval

Incorruptible Domain References

Surgical Retry

Pre-execution Guardrails

Observability

Built for the Most Demanding Enterprise Environments

Compliant

Security-Hardened

Fully Auditable

Scalable

Self-Correcting

Model-Agnostic

Deployment-Flexible

Here are some seed ideas for illustration, all of which our clients and partners across private and public US companies already use our platform for successfully. Silverlake supports any shape you can imagine

Document Analysis at Scale

Multi-Agent Research Workflows

End-to-End Process Automation

Institutional Knowledge Capture

Ready to Automate Your Most
Complex Workflows?

An Enterprise Agentic System is, Above All, an Enterprise System

Silverlake Maps Agentic Concerns onto Enterprise Primitives

What agentic systems add is a new layer of concerns the enterprise playbook has not had to formalize, posing genuine new engineering problems:

Agents and Tools

Integrations

Deterministic Guardrails

Uncorruptible Domain Objects

Resource Management

Inference Optimization

Intelligent Compaction

Field Summarization

Two-Tier Retrieval

Incorruptible Domain References

Surgical Retry

Pre-execution Guardrails

Observability

Built for the Most Demanding Enterprise Environments

Compliant

Security-Hardened

Fully Auditable

Scalable

Self-Correcting

Model-Agnostic

Deployment-Flexible

Here are some seed ideas for illustration, all of which our clients and partners across private and public US companies already use our platform for successfully. Silverlake supports any shape you can imagine

Document Analysis at Scale

Multi-Agent Research Workflows

End-to-End Process Automation

Institutional Knowledge Capture

Ready to Automate Your MostComplex Workflows?

An Enterprise Agentic System is, Above All,
an Enterprise System

Ready to Automate Your Most
Complex Workflows?