Agentic AI Solution Development

Most organizations can build an AI agent demo. Far fewer can run agents in production that act on real systems, respect user permissions, and stay accurate, secure, and cost-controlled. This 10-week Implementation engagement designs, builds, and deploys one to two governed AI agents on AWS, plus a reusable foundation for the agents that follow.

We start with business analysis, not code. Each agent is scoped to a specific workflow with a defined outcome, the data it needs, and clear points where a human approves, escalates, or overrides.

• Amazon Bedrock for foundation models, with Bedrock Guardrails for content filtering, PII protection, and policy enforcement
• Amazon Bedrock AgentCore for agent runtime, memory, identity, and observability
• AgentCore Gateway and Amazon API Gateway to turn existing APIs and Lambda functions into governed agent tools
• Bedrock Knowledge Bases with OpenSearch Serverless or S3 Vectors for grounded retrieval over enterprise knowledge
• IAM Identity Center or your identity provider for per-agent identity and on-behalf-of access, so agents act with the user's own permissions
• Lambda, ECS on Fargate, EventBridge, Step Functions, and SQS for scalable, event-driven workloads
• Integration with data on Amazon S3, Amazon Redshift, or Databricks on AWS

• PHASE 1: Strategy Workshop. Confirm workflows, success metrics, stakeholders, and human-in-the-loop requirements
• PHASE 2: Architecture Design. Define the AWS reference architecture, identity model, security controls, and integrations, aligned to the AWS Well-Architected Framework
• PHASE 3: Knowledge and Context Engineering. Prepare data, build retrieval indexes, and expose APIs as secure agent tools (adding OAuth where systems use static keys)
• PHASE 4: Agent Development. Build, integrate, and iterate in a non-production environment with user feedback
• PHASE 5: Evaluation and Governance. Evaluate groundedness, accuracy, and task completion; configure guardrails, audit logging, and trust levels
• PHASE 6: Production Rollout. Deploy, enable monitoring and cost alerts, and hand off to your team

• Agent-ready AWS environment with VPC isolation, private endpoints, and AWS Secrets Manager
• Bedrock model access, guardrails, and AgentCore runtime configuration
• Governed, OAuth-based tool layer for model, tool, and agent traffic
• Per-agent identities and delegated access tied to your identity provider
• Tracing, evaluation pipeline, and per-agent cost attribution via AgentCore Observability, Amazon CloudWatch, and cost allocation tags

• Production-ready/deployed AI agent
• Workflow decomposition and agent design documentation
• AWS reference architecture and architectural decision records
• Reusable tool layer for connected business systems
• Evaluation test suite with baseline quality results
• Monitoring dashboards and cost-attribution reporting
• Operations runbook and knowledge transfer sessions
• Roadmap for scaling to additional agents

• Agents in production completing real work inside your existing systems
• Built-in security and governance: scoped identity, audit trails, guardrails, and human oversight
• Clear visibility into agent quality, usage, and spend
• A repeatable foundation that lowers the time and cost of each additional agent

Organizations on AWS with validated AI use cases, or a completed proof of concept, ready to put governed agents into production.

27Global is an AWS Advanced Tier Services Partner delivering software, cloud, data, and AI engineering for mid-market and enterprise organizations. 27Global holds the AWS SaaS and Data Competencies, and the AWS Well-Architected Partner designation.

Pricing is based on scope, including number of agents, systems integrated, and data readiness. Request a private offer for a fixed-scope proposal.