Implementation Plan

12-month phased roadmap for building the agent infrastructure and deploying all 209 agents

Timeline Overview

12 months

Total duration

$600K

Development cost

4-6

Engineers needed

4 phases

Incremental rollout

Phase 1: Core Agent Runtime + Orchestration

Months 1-3

Build the foundation: agent execution, context management, and orchestration

3 months

4 engineers

$150K

Deliverables

Agent Runtime (Node.js + TypeScript, Anthropic API integration)

Context Management (Redis for short-term, PostgreSQL + S3 for long-term)

Model Routing (Haiku/Sonnet/Opus selection based on task complexity)

Orchestration Platform (Temporal.io for multi-agent workflows)

Agent API Gateway (Kong for unified external service access)

Deploy first 3 agents (Withdrawal Agent, KYC Agent, Deposit Agent)

Success Metrics

Agent execution success rate>95%

Average response time<5 seconds

Cost per task<$0.10

Phase 2: Human Oversight + Audit

Months 4-6

Build dashboards for human operators to monitor and override agents

3 months

3 engineers

$120K

Deliverables

Back Office Dashboard (real-time agent activity, approval queue, manual overrides)

Approval Workflows (4-tier system with multi-person approval)

Audit & Compliance Dashboard (immutable logs, blockchain-backed critical actions)

Agent Performance Dashboard (approval rate, override rate, error rate, cost metrics)

Deploy 50 more agents (Risk, Compliance, Finance, Customer Support)

Success Metrics

Human override rate<5%

Approval SLA<5 minutes

Audit log completeness100%

Phase 3: Developer Tools + Testing

Months 7-9

Build tools to test, debug, and optimize agents before deployment

3 months

2 engineers

$90K

Deliverables

Agent Testing Framework (unit, integration, regression, load tests)

Agent Playground (interactive testing with cost calculator)

Workflow Builder (drag-and-drop visual workflow designer)

Agent Versioning (canary deployments, A/B testing, one-click rollback)

Deploy 100 more agents (Marketing, Product, Casino Operations)

Success Metrics

Test coverage>80%

Deployment frequencyDaily

Rollback time<5 minutes

Phase 4: Scale + Optimize

Months 10-12

Optimize for scale, reduce costs, and deploy remaining agents

3 months

3 engineers

$120K

Deliverables

Model Fine-tuning (train custom models for high-volume tasks)

Cost Optimization (reduce token usage, optimize model routing)

Performance Tuning (reduce latency, increase throughput)

Data Warehouse (Snowflake for analytics and ML training data)

Deploy remaining 56 agents (all 209 agents live)

Success Metrics

Cost per task<$0.044

Throughput>10K tasks/day

Agent error rate<1%

Team Structure

Roles and responsibilities for the implementation team

Core Team (4-6 engineers)

Backend Engineers2

Frontend Engineer1

DevOps Engineer1

ML Engineer (optional)1

QA Engineer (optional)1

Key Skills Required

Node.jsTypeScriptReactPostgreSQLRedisKafkaAWSDockerTemporal.ioLLM APIs

Hiring Strategy

Start with 2 backend + 1 frontend + 1 DevOps (4 engineers). Add ML and QA engineers in Phase 3-4 as needed. Total cost: $50K/month.

Risk Mitigation

Key risks and mitigation strategies

Risk: Agent quality issues

Mitigation: Start with 3 agents in Phase 1, test thoroughly, iterate based on feedback. Add human oversight in Phase 2.

Risk: LLM API costs spike

Mitigation: Implement model routing (70% Haiku, 25% Sonnet, 5% Opus). Fine-tune custom models in Phase 4 for high-volume tasks.

Risk: Regulatory concerns

Mitigation: Build audit logs and human oversight in Phase 2. All agent decisions are logged and can be overridden by humans.

Risk: Team capacity

Mitigation: Start with 4 engineers, add 2 more in Phase 3-4. Use external contractors for specialized work (ML, blockchain).

Next Steps

1. Validate with CTO and stakeholders

Review architecture, ROI, and implementation plan

2. Hire core team (4 engineers)

2 backend, 1 frontend, 1 DevOps

3. Start Phase 1 (Months 1-3)

Build agent runtime, orchestration, and deploy first 3 agents

4. Iterate based on feedback

Monitor agent performance, collect operator feedback, optimize