AccelerateDelivery.Own YourTechnology.

import { NeuralNet, VectorDatabase, AgentTool, InferenceOptions, AgentResponse } from "@colonyone/ai-engine";
import { BaseAgent, IReasoningEngine, IMemoryProvider, IToolOrchestrator, BaseContext } from "@colonyone/agent-framework";
import { TransformerConfig, PgVectorBackend, ReasoningContext } from "@colonyone/ml-types";
 
export class EnterpriseAgent<TContext extends BaseContext> extends BaseAgent implements IReasoningEngine, IMemoryProvider, IToolOrchestrator {
  private readonly model: NeuralNet<TransformerConfig>;                              // 24-layer transformer backbone with flash-attention-v2 and rotary positional embeddings
  private readonly memory: VectorDatabase<Float32Array, PgVectorBackend>;           // persistent pgvector store with HNSW indexing — 1M+ document capacity at sub-10ms retrieval
  private readonly toolRegistry: Map<string, AgentTool<unknown>> = new Map();          // dynamically registered agent tools — code-exec, web-search, database-query, api-call
 
  async reason(context: TContext, options?: InferenceOptions): Promise<AgentResponse<TContext>> {                        // main reasoning loop — chain-of-thought with tool-use orchestration
    const embedding     = await this.model.encode(context.prompt, { maxTokens: 4096, pooling: "mean" });             // encode input to 1024-dim vector
    const relevantDocs  = await this.memory.similaritySearch(embedding, { topK: 10, threshold: 0.82, rerank: true });   // RAG retrieval with re-ranking
    const toolResults   = await this.executeTools(context.requiredTools, relevantDocs);                               // execute matched tools in parallel
    return this.synthesize(relevantDocs, toolResults, context, options?.temperature ?? 0.7);                   // generate final response
  }
}

import { DeploymentOrchestrator, KubernetesTarget, DockerRegistry, RollbackPolicy } from "@colonyone/deploy-orchestrator";
 
const deploymentPipeline = new DeploymentOrchestrator<KubernetesTarget, DockerRegistry>({
  name:              "colonyone-platform-release-v2",                                                                    // release identifier
  stages:            ["lint", "build", "unit-test", "integration", "security-scan", "canary-5%", "canary-25%", "production-100%"],
  infrastructure:    { replicas: 3, gpu: "nvidia-a100-80gb", region: "ap-southeast-7", nodePool: "ml-inference" },
  rollbackPolicy:   { automatic: true, errorBudget: 0.01, monitoringWindow: "5m", strategy: "instant-rollback" },
});
 
const deployResult = await deploymentPipeline.execute({ dryRun: false, notifySlack: "#ops-deploy", approvers: ["lead", "sre"] });
logger.info(`✓ Deploy ${deployResult.version} → ${deployResult.target} completed in ${deployResult.duration}ms [${deployResult.stage}]`);

import type { PlatformConfig, SemVer, HealthEndpoint, SLAConfig } from "@colonyone/platform-registry";
 
interface PlatformConfig<TEngine extends string = "multi-agent" | "rag" | "hybrid" | "fine-tuned"> {
  name: string;         engine: TEngine;       accuracy: number;       status: "active" | "beta" | "maintenance";
  endpoints: Record<string, URL>;   version: SemVer;   healthCheck: HealthEndpoint;   sla: SLAConfig;
}
 
const registeredPlatforms: ReadonlyArray<PlatformConfig> = [                                                    // immutable platform registry
  { name: "Colony One",   engine: "multi-agent",  accuracy: 0.97,  status: "active",       version: "2.4.1"  },  // unified business platform
  { name: "BackQ.io",    engine: "rag",           accuracy: 0.95,  status: "active",       version: "1.8.0"  },  // managed service platform
  { name: "Conan",       engine: "hybrid",        accuracy: 0.93,  status: "active",       version: "1.2.0"  },  // customer data intelligence
  { name: "Gotham",      engine: "multi-agent",  accuracy: 0.91,  status: "beta",         version: "0.9.2"  },  // digital asset management
  { name: "Edison",      engine: "fine-tuned",   accuracy: 0.89,  status: "beta",         version: "0.7.1"  },  // telesales platform
] as const;

import { analyzeTotalCostOfOwnership, designSystemArchitecture, buildWithMultiAgentFramework, migrateDataAndUsers } from "@colonyone/saas-migration";
 
export async function replaceLegacySaaS(legacy: SaaSApplication, requirements: BusinessRequirement[]): Promise<OwnedPlatform> {
  const costAnalysis     = await analyzeTotalCostOfOwnership(legacy.subscription, legacy.userCount, legacy.contractTermYears);
  const systemDesign     = await designSystemArchitecture(requirements, costAnalysis.projectedSavings, { cloudNative: true });
  const ownedPlatform    = await buildWithMultiAgentFramework(systemDesign, { agents: 12, parallelism: 4, cicd: true });
  await migrateDataAndUsers(legacy, ownedPlatform, { zeroDowntime: true, validateIntegrity: true, rollbackOnError: true });
  return ownedPlatform;                                    // customer owns 100% of the platform — zero recurring SaaS fees, full IP ownership
}

import { trainAndValidate, TrainingPipelineConfig, DistributedStrategy, CheckpointConfig } from "@colonyone/ml-training-pipeline";
 
const trainingConfig: TrainingPipelineConfig<"transformer"> = {                                                     // typed training config
  model: "transformer-xl-enterprise",    architecture: "encoder-decoder",    precision: "bf16",                  // mixed precision training
  layers: 24,    hiddenSize: 1024,    heads: 16,    attention: "flash-attention-v2",    contextLength: 32768,
  dropout: 0.1,   batchSize: 32,      epochs: 100,  optimizer: "adamw",    scheduler: "cosine-warmup",   lr: 3e-4,
  distributed: { strategy: "deepspeed-zero3", gpus: 8, nodes: 2, gradientCheckpointing: true },          // multi-node distributed training
};
 
const trainedModel = await trainAndValidate(trainingConfig, dataset, { earlyStopping: true, patience: 5, checkpoint: "s3://models/" });
logger.info(`✓ Training complete — accuracy: ${trainedModel.metrics.accuracy.toFixed(4)}, F1: ${trainedModel.metrics.f1}, loss: ${trainedModel.metrics.loss}`);

import { NeuralNet, VectorDatabase, AgentTool, InferenceOptions, AgentResponse } from "@colonyone/ai-engine";
import { BaseAgent, IReasoningEngine, IMemoryProvider, IToolOrchestrator, BaseContext } from "@colonyone/agent-framework";
import { TransformerConfig, PgVectorBackend, ReasoningContext } from "@colonyone/ml-types";
 
export class EnterpriseAgent<TContext extends BaseContext> extends BaseAgent implements IReasoningEngine, IMemoryProvider, IToolOrchestrator {
  private readonly model: NeuralNet<TransformerConfig>;                              // 24-layer transformer backbone with flash-attention-v2 and rotary positional embeddings
  private readonly memory: VectorDatabase<Float32Array, PgVectorBackend>;           // persistent pgvector store with HNSW indexing — 1M+ document capacity at sub-10ms retrieval
  private readonly toolRegistry: Map<string, AgentTool<unknown>> = new Map();          // dynamically registered agent tools — code-exec, web-search, database-query, api-call
 
  async reason(context: TContext, options?: InferenceOptions): Promise<AgentResponse<TContext>> {                        // main reasoning loop — chain-of-thought with tool-use orchestration
    const embedding     = await this.model.encode(context.prompt, { maxTokens: 4096, pooling: "mean" });             // encode input to 1024-dim vector
    const relevantDocs  = await this.memory.similaritySearch(embedding, { topK: 10, threshold: 0.82, rerank: true });   // RAG retrieval with re-ranking
    const toolResults   = await this.executeTools(context.requiredTools, relevantDocs);                               // execute matched tools in parallel
    return this.synthesize(relevantDocs, toolResults, context, options?.temperature ?? 0.7);                   // generate final response
  }
}

import { DeploymentOrchestrator, KubernetesTarget, DockerRegistry, RollbackPolicy } from "@colonyone/deploy-orchestrator";
 
const deploymentPipeline = new DeploymentOrchestrator<KubernetesTarget, DockerRegistry>({
  name:              "colonyone-platform-release-v2",                                                                    // release identifier
  stages:            ["lint", "build", "unit-test", "integration", "security-scan", "canary-5%", "canary-25%", "production-100%"],
  infrastructure:    { replicas: 3, gpu: "nvidia-a100-80gb", region: "ap-southeast-7", nodePool: "ml-inference" },
  rollbackPolicy:   { automatic: true, errorBudget: 0.01, monitoringWindow: "5m", strategy: "instant-rollback" },
});
 
const deployResult = await deploymentPipeline.execute({ dryRun: false, notifySlack: "#ops-deploy", approvers: ["lead", "sre"] });
logger.info(`✓ Deploy ${deployResult.version} → ${deployResult.target} completed in ${deployResult.duration}ms [${deployResult.stage}]`);

import type { PlatformConfig, SemVer, HealthEndpoint, SLAConfig } from "@colonyone/platform-registry";
 
interface PlatformConfig<TEngine extends string = "multi-agent" | "rag" | "hybrid" | "fine-tuned"> {
  name: string;         engine: TEngine;       accuracy: number;       status: "active" | "beta" | "maintenance";
  endpoints: Record<string, URL>;   version: SemVer;   healthCheck: HealthEndpoint;   sla: SLAConfig;
}
 
const registeredPlatforms: ReadonlyArray<PlatformConfig> = [                                                    // immutable platform registry
  { name: "Colony One",   engine: "multi-agent",  accuracy: 0.97,  status: "active",       version: "2.4.1"  },  // unified business platform
  { name: "BackQ.io",    engine: "rag",           accuracy: 0.95,  status: "active",       version: "1.8.0"  },  // managed service platform
  { name: "Conan",       engine: "hybrid",        accuracy: 0.93,  status: "active",       version: "1.2.0"  },  // customer data intelligence
  { name: "Gotham",      engine: "multi-agent",  accuracy: 0.91,  status: "beta",         version: "0.9.2"  },  // digital asset management
  { name: "Edison",      engine: "fine-tuned",   accuracy: 0.89,  status: "beta",         version: "0.7.1"  },  // telesales platform
] as const;

import { analyzeTotalCostOfOwnership, designSystemArchitecture, buildWithMultiAgentFramework, migrateDataAndUsers } from "@colonyone/saas-migration";
 
export async function replaceLegacySaaS(legacy: SaaSApplication, requirements: BusinessRequirement[]): Promise<OwnedPlatform> {
  const costAnalysis     = await analyzeTotalCostOfOwnership(legacy.subscription, legacy.userCount, legacy.contractTermYears);
  const systemDesign     = await designSystemArchitecture(requirements, costAnalysis.projectedSavings, { cloudNative: true });
  const ownedPlatform    = await buildWithMultiAgentFramework(systemDesign, { agents: 12, parallelism: 4, cicd: true });
  await migrateDataAndUsers(legacy, ownedPlatform, { zeroDowntime: true, validateIntegrity: true, rollbackOnError: true });
  return ownedPlatform;                                    // customer owns 100% of the platform — zero recurring SaaS fees, full IP ownership
}

import { trainAndValidate, TrainingPipelineConfig, DistributedStrategy, CheckpointConfig } from "@colonyone/ml-training-pipeline";
 
const trainingConfig: TrainingPipelineConfig<"transformer"> = {                                                     // typed training config
  model: "transformer-xl-enterprise",    architecture: "encoder-decoder",    precision: "bf16",                  // mixed precision training
  layers: 24,    hiddenSize: 1024,    heads: 16,    attention: "flash-attention-v2",    contextLength: 32768,
  dropout: 0.1,   batchSize: 32,      epochs: 100,  optimizer: "adamw",    scheduler: "cosine-warmup",   lr: 3e-4,
  distributed: { strategy: "deepspeed-zero3", gpus: 8, nodes: 2, gradientCheckpointing: true },          // multi-node distributed training
};
 
const trainedModel = await trainAndValidate(trainingConfig, dataset, { earlyStopping: true, patience: 5, checkpoint: "s3://models/" });
logger.info(`✓ Training complete — accuracy: ${trainedModel.metrics.accuracy.toFixed(4)}, F1: ${trainedModel.metrics.f1}, loss: ${trainedModel.metrics.loss}`);