Migrating large applications and modernizing legacy systems often involves complex planning, labor-intensive processes, and risks that can stall timelines. Lately, AWS Partners have begun embedding agentic AI, autonomous systems that execute complex tasks with minimal human input, into their migration and modernization toolkits. These next-generation solutions reduce manual effort across discovery, analysis, planning, and execution phases, accelerating cloud adoption for enterprise customers.

This blog explains how AWS Partners use agentic AI, the advantages they attain, and key considerations for organizations exploring these approaches.

Understanding Agentic AI in a Cloud Migration Context

Traditional automation typically relies on scripted workflows or rule-based engines. By contrast, agentic AI combines large language models (LLMs), reinforcement learning, and microservices architectures to create goal-driven “agents.” These agents can plan, iterate, and deliver outcomes based on high-level objectives. In a migration or modernization exercise, an agent might:

• Ingest application binaries, documentation, and operational logs.
• Reconstruct an application’s dependency map.
• Propose a migration roadmap, generate code templates for refactoring, or orchestrate infrastructure provisioning.
• Run validation tests and adjust configurations autonomously.
• Several developments have made agentic AI more viable for AWS Partners:

1. Mature Model Foundations

AWS’s Titan series and other pretrained models provide the natural language understanding and reasoning capabilities required to interpret unstructured documentation, configuration files, and application code. Partners fine-tune these models on domain-specific data, such as finance, healthcare, or manufacturing, to improve accuracy in legacy contexts.

2. Integrated Toolchains

AWS offers frameworks such as SageMaker JumpStart Agents and Amazon CodeWhisperer. Partners build on these to hook AI agents into CI/CD pipelines, infrastructure-as-code templates, and observability platforms. Native AWS services, such as AWS Step Functions, AWS Lambda, AWS CloudFormation, provide the scaffolding for agents to take autonomous actions (for example, reconfiguring a load balancer or initiating a database schema migration).

3. Partner-Layered Observability and Security

Through AWS Marketplace and the AWS Well-Architected Framework, partners embed logging, monitoring, and governance controls into their agentic solutions. By combining AWS Security Hub, Amazon GuardDuty, and partner-provided policy engines, agents can enforce compliance at each step of migration.

How Partners Embed Agentic AI into Migration Workflows

AWS Partners often specialize in discrete phases of migration and modernization. Below are examples of how agentic AI integrates into each stage:

1. Automated Discovery and Assessment

• Legacy Inventory Extraction
  Agents scan existing repositories (source control, on-premises servers, virtual machines) to compile an inventory of applications, services, databases, and dependencies. Using NLP, they parse README files, code comments, and operational logs to reconstruct a dependency graph.
• Risk Scoring and Prioritization
  By correlating application age, technology stack, business criticality, and performance metrics, agents generate a prioritized list of workloads for migration. These risk scores guide customers on which applications to refactor first and which can be rehosted (“lift-and-shift”).

2. Automated Refactoring and Transformation

• Code Analysis and Transformation
  Agentic AI examines code repositories, identifies deprecated libraries or on-premises-only APIs, and recommends modern alternatives—such as migrating a local JDBC connection to Amazon RDS or replacing a self-managed Redis cache with Amazon ElastiCache.
• Template Generation
  After code analysis, agents generate boilerplate infrastructure as code (IaC) templates, application skeletons (e.g., container definitions or serverless functions), and CI/CD pipeline configurations. This scaffolding enables developers to spin up cloud environments and begin testing faster.

3. Infrastructure Provisioning and Validation

• Autonomous Orchestration
  Agents leverage AWS CloudFormation, Terraform (via partner integrations), or AWS CDK to provision networks, compute resources, storage, and security controls. Once infrastructure is deployed, agents run validation checks to verify that network ACLs, IAM roles, and security group rules align with best practices.
• Cost Optimization
  Using historical usage patterns and performance metrics from on-premises environments, agents estimate appropriate instance types, storage tiers, and reserved instance options. This proactive forecasting helps customers minimize cloud expenditure from the outset.

4. Continuous Migration Refinement

• Iterative Testing and Rollback
  As workloads move, agents run functional and load-testing scripts to validate performance. If a service underperforms—for example, latency spikes beyond defined thresholds—agents trigger rollback procedures or recommend horizontal scaling adjustments.
• Feedback Loops with Development and Operations Teams
  Agents provide annotated reports—highlighting code fragments, configuration changes, and performance deltas—so teams can make targeted refinements. These feedback loops reduce time spent diagnosing complex migration failures.

5. Post-Migration Modernization

• Intelligent Cost Governance
  After migration, agents monitor resource utilization, identifying idle assets (old EBS volumes, underutilized EC2 instances) and recommending rightsizing. They can also schedule non-production environments to shut down during off-hours.
• Security Remediation
  Agents continuously scan for post-migration drift: outdated SSL/TLS certificates, misconfigured security groups, and IAM anomalies. When issues arise, agents either apply patches autonomously or generate pull requests for DevSecOps teams to review.

Benefits of Agentic AI for Migration and Modernization

• Reduced Manual Overhead
  Agents handle repetitive tasks—code scanning, dependency mapping, template generation—freeing engineers to focus on architectural decisions and integration.
• Consistency and Repeatability
  By codifying best practices into autonomous workflows, agents ensure uniform execution across workloads and minimize human error.
• Faster Time to Value
  Automatically generated IaC templates and test harnesses let development and QA teams validate cloud environments earlier in the process, shrinking the gap between assessment and production.
• Proactive Risk Mitigation
  Continuous monitoring and validation detect misconfigurations or performance regressions early, before they become outages.
• Scalable Expertise
  Agents encapsulate partner expertise—whether in financial compliance or healthcare regulations—enabling even smaller IT teams to execute complex migrations with confidence.

Key Considerations for Evaluation

While agentic AI can significantly accelerate migration, organizations should weigh the following factors:

• Data Privacy and Compliance
  Ensure that agents operate within robust data governance frameworks. Partners should provide detailed documentation on data ingestion, processing, and storage—especially for regulated industries.
• Transparency and Auditability
  Autonomous agents must maintain logs and decision-making trails. Customers should be able to review every change an agent proposes or executes. Partners often integrate AWS CloudTrail, AWS Config, and custom dashboards to support audits.
• Scope and Limitations
  Some legacy workloads—particularly those using proprietary or unsupported frameworks—may require manual intervention. Early assessment should identify which applications can be fully automated versus those needing specialist teams.
• Skillset Alignment
  Although agents reduce repetitive tasks, teams still need cloud architects and DevOps engineers to review recommendations, troubleshoot edge cases, and enforce governance. Invest in training to ensure smooth adoption.
• Cost-Benefit Analysis
  Agentic frameworks often incur usage-based charges for inference, data processing, and managed services. Conduct a total cost of ownership (TCO) analysis comparing licensing fees and AWS service consumption against savings in personnel hours and faster time to market.

Best Practices for Working with AWS Partners on Agentic AI

• Start with a Pilot
  Select a non-critical workload to validate the agentic approach. Use this pilot to confirm the reliability of automated decisions, refine risk-scoring criteria, and establish governance guardrails.
• Define Clear Success Metrics
  Whether it’s percentage reduction in migration time, application performance improvements, or compliance coverage, measurable objectives help align partner deliverables with business goals.
• Build Cross-Functional Teams
  Engage stakeholders from security, operations, compliance, and development to ensure agents address all concerns. These teams should review agent-generated reports and provide feedback for continuous improvement.
• Leverage AWS Well-Architected Reviews
  Agents can generate Well-Architected compliance checks, but periodic manual reviews by certified AWS architects help validate that automated assessments align with organizational priorities.
• Maintain Human Oversight
  Even the most capable agents benefit from human validation. Establish thresholds for autonomous changes—for example, allowing agents to deploy minor configuration adjustments automatically but requiring approval for database schema modifications.

The Road Ahead

As AWS continues refining its AI-centric services—introducing new agent frameworks, improving model performance, and deepening integrations with managed services—AWS Partners will expand their portfolios of agentic solutions.

We can anticipate:

• Vertical-Specific Agent Marketplaces
  Partners may offer pre-trained agents tailored for finance, manufacturing, retail, and other sectors, enabling customers to bypass extensive customization.
• Self-Service Agent Builders
  Small and mid-sized businesses may access agentic workflows through low-code interfaces, democratizing advanced migration and modernization capabilities.
• Dynamic Compliance Agents
  Agents that not only assess and remediate compliance issues but also adapt in real time to changes in regulatory requirements—delivering continuous, automated governance.
  Together, these trends suggest a future where cloud migration and legacy modernization no longer hinge on weeks of manual scripting and validation. Instead, organizations can rely on AWS Partner-provided agents to manage routine tasks autonomously, allowing teams to innovate on new features, improve user experiences, and accelerate time to market.

Final Thoughts

Agentic AI is reshaping how AWS Partners support migration and legacy modernization. By combining pretrained models, integrated AWS services, and partner expertise, autonomous agents can manage everything from discovery and assessment to infrastructure provisioning, testing, and ongoing governance. The result is a streamlined, repeatable methodology that reduces manual overhead, mitigates risk, and shortens migration timelines.

Organizations evaluating agentic AI should focus on data security, auditability, and total cost of ownership. Pilot projects help validate the effectiveness of autonomous approaches, while close collaboration with AWS Partners ensures continuous refinement. As these technologies evolve, agentic AI will become a core component in every cloud modernization strategy—helping enterprises unlock the full potential of AWS with greater speed and confidence.