Clavis Technologies Builds an Agentic AI Platform on AWS to Accelerate the Full Software Development Lifecycle
Clavis Technologies partnered with CLAVIS TECHNOLOGIES Digital to build an agentic, AI-assisted SDLC platform on Amazon Bedrock — one that turns requirements into user stories and architecture decisions, generates code, tests and CI/CD pipelines, and enforces DevSecOps security gates, with engineers still reviewing and approving everything that is merged or released. The platform runs on a multi-AZ AWS architecture with cross-region disaster recovery.
One platform for the whole delivery team, from requirement to release
Clavis Technologies is a software-development organization whose product owners, architects, developers, QA engineers and DevSecOps teams move work from an initial requirement through design, build, security review and release. Its platform serves four groups of users — business users, developers, DevOps and security teams, and platform administrators — through a React multi-tenant dashboard, Jira, GitHub, CI/CD pipelines, and API/webhook integrations.
To shorten the development lifecycle without loosening engineering control, Clavis Technologies engaged CLAVIS TECHNOLOGIES Digital, an AWS Generative AI Competency Partner specializing in agentic AI/ML platforms for enterprise and public-sector clients, to design and implement an AI-assisted SDLC platform on AWS. A Planner Agent and a team of Specialist AI Agents on Amazon Bedrock automate requirement analysis, architecture recommendations, code and unit-test generation, CI/CD pipeline creation and security gating — grounded in Clavis Technologies’ own standards and past decisions, and with engineers in the loop to review and approve the results.
Delivery speed was limited by manual hand-offs between disconnected tools
Software delivery at Clavis Technologies depended on a chain of manual steps: analyzing requirements, making architecture decisions, writing boilerplate code and tests, assembling pipelines per project, and chasing security findings late in the cycle. Each step lived in a different tool, and each hand-off added delay, rework and audit effort.
Slow starts
Manual requirement analysis and design delayed the start of build work and slowed iterations and time-to-release.
Requirement-to-code drift
Inconsistencies between business requirements and technical implementation led to rework and misaligned releases.
Senior-engineer bottleneck
Architecture decisions leaned heavily on a few senior engineers, creating a design bottleneck and slowing developer onboarding.
Fragmented tooling
Work was spread across Jira, GitHub, CI/CD and security platforms, with no single view of delivery and limited traceability.
Late security findings
Security issues surfaced late in the cycle, causing rework and release bottlenecks.
Heavy governance effort
Gathering audit evidence and managing governance across teams and projects was largely manual.
Integration and isolation constraints
Any new platform had to work with existing Jira, GitHub and Jenkins workflows, keep tenants isolated from one another, and protect source code, requirements and security findings with encryption in transit and at rest.
A Planner Agent and specialist agents across the six stages of delivery
CLAVIS TECHNOLOGIES Digital designed a containerized, agentic platform on AWS. A Planner Agent on Amazon Bedrock delegates work to Specialist AI Agents — requirements, architecture, code and test generation, and DevSecOps — and combines their results. The agents retrieve Clavis Technologies’ coding standards, reference architectures, ADRs, templates and security policies from a vector knowledge base, so every output reflects the organization’s own practices rather than generic model knowledge.
Requirements Analysis
AI-generated user stories and acceptance criteria, synchronized with Jira.
Design & Architecture
Automated architecture recommendations and Architecture Decision Records (ADRs), grounded in the engineering knowledge base.
Code Generation
Python, Node.js and React code with automated unit tests, raised as pull requests in GitHub.
Build & Deploy
Automated CI/CD pipelines and environment configuration templates, run through Jenkins and AWS CodeBuild.
Security & Compliance
SAST/DAST integration with policy-based security gates and SBOM generation on every build.
Governance & Monitoring
Multi-tenant RBAC, audit logs and delivery analytics dashboards for traceability and compliance.
Engineers stay in control throughout: generated stories, designs, code and pipelines are reviewed by engineers, pass the same security gates as human-written code, and the decision to approve, merge or release is never automated.
Security is built in from the first step rather than added at release. Security requirements are captured alongside user stories, and ADRs record security design decisions grounded in Clavis Technologies’ policies. Generated code retrieves the organization’s security standards and uses AWS Secrets Manager instead of hard-coded credentials. Every build passes through SAST/DAST gates and SBOM generation, infrastructure is defined as code and evaluated continuously by AWS Config, and releases use Blue-Green or Canary deployments on Amazon ECS.
Users and integrations reach the platform over HTTPS through Amazon Route 53, AWS WAF and AWS Certificate Manager to an Application Load Balancer in the public subnet of a multi-AZ VPC. The React frontend and the Node.js and Python microservices run on Amazon ECS with AWS Fargate in a private application subnet, calling the Planner and Specialist Agents on Amazon Bedrock, which reason over a vector database and store artifacts and logs in Amazon S3 in a private AI/ML & data subnet.
A private CI/CD & security subnet hosts GitHub integration, Jenkins, Amazon ECR, Amazon EKS, SAST/DAST scanning and AWS CodeBuild. A security and governance layer (AWS IAM, AWS Organizations, AWS Secrets Manager, AWS Config, and audit logs in Amazon S3) and a management and observability layer (Amazon CloudWatch, AWS CloudTrail, AWS X-Ray and AWS KMS) wrap the environment, with Amazon SNS delivering notifications and alerts.
The building blocks
| Capability | AWS Service(s) | Role in the Solution |
|---|---|---|
| Agentic reasoning & orchestration | Amazon Bedrock (Planner Agent & Specialist AI Agents) | Provides managed foundation-model access; the Planner Agent delegates to requirements, architecture, code and test, and DevSecOps agents, and combines their results. |
| Engineering knowledge (RAG) | Vector databaseAmazon S3 | Indexes standards, ADRs, templates and pipeline definitions, scoped by tenant and project, so outputs follow Clavis Technologies’ own practices. |
| Application services | Amazon ECS with AWS Fargate | Runs the React frontend and Node.js and Python microservices as containers that scale independently, without server management. |
| Traffic & edge protection | Amazon Route 53AWS Shield StandardAWS WAFAWS Certificate ManagerApplication Load Balancer | Resolves DNS, absorbs infrastructure-layer attacks, filters web exploits and rate-limits abusive clients, provisions TLS certificates and distributes traffic across Availability Zones. |
| CI/CD & build | GitHubJenkinsAWS CodeBuildAmazon ECRAmazon EKS | Builds, tests, scans and packages changes; stores container images and provides the Kubernetes environment for the CI/CD and security layer. |
| Application security | SAST/DAST toolsSBOM generation | Applies policy-based security gates to every build so vulnerabilities and dependency risk are caught early. |
| Data & artifact storage | Amazon S3 | Stores training data, generated artifacts, ADRs, SBOMs, logs, reports and knowledge-base source documents. |
| Identity & governance | AWS IAMAWS OrganizationsAWS STS | Enforces least-privilege access and multi-tenant RBAC with short-lived credentials. |
| Encryption & secrets | AWS KMSAWS Secrets Manager | Encrypts data at rest and holds Jira, GitHub and Jenkins credentials so no secrets are hard-coded. |
| Network isolation | Amazon VPCSecurity groupsNetwork ACLsVPC endpoints | Segments public, application, AI/ML & data, and CI/CD & security tiers with private connectivity to AWS services. |
| Infrastructure as code & compliance | AWS CloudFormation or TerraformAWS Config | Defines network, IAM, KMS and compute resources as version-controlled code deployed to both regions, and continuously evaluates configuration against compliance rules. |
| Monitoring & observability | Amazon CloudWatchAWS X-RayAWS CloudTrailAmazon SNS | Provides metrics, dashboards, tracing, API audit logging and alert notifications. |
| Disaster recovery | Amazon Route 53S3 Cross-Region ReplicationAmazon ECR replicationAWS Secrets Manager replicasAWS Backup | Enables cross-region failover and scheduled backup and restore for business continuity. |
| Cost management | AWS BudgetsAWS Cost ExplorerAWS Cost Anomaly Detection | Attributes spend to tenants, environments and workflow stages, and alerts on budget overruns and unexpected spikes. |
Protection matched to how much each capability matters
The platform is built from managed, multi-AZ services, and Clavis Technologies decided in advance which capabilities must keep running and which may degrade. Availability is delivered through five layers of protection:
Redundancy
The VPC, subnets, Application Load Balancer, Amazon ECS services on AWS Fargate and data stores span two or more Availability Zones, and the load balancer routes only to healthy targets.
Decoupling
Repository-wide code generation, pipeline creation and scans run as asynchronous jobs, so the dashboard and APIs stay responsive, and a slow downstream step does not block users.
Graceful degradation
If Amazon Bedrock, Jira, GitHub, Jenkins or a scanner is unavailable, services retry with back-off, queue work and fall back to manual workflows, while the dashboard, audit trail and existing artifacts remain available.
Elasticity
ECS services scale independently with demand, and caching and scoped agent context limit the load on the vector database and Amazon Bedrock during peaks.
Recovery
Cross-region replication, AWS Backup and a warm-standby DR region restore service after a regional disruption.
Capabilities are grouped into three tiers so that the strongest protection is applied where an outage matters most.
Critical
CapabilitiesDashboard and API access, authentication, webhook receipt, audit logging and access to existing artifacts.
Behavior under failureMulti-AZ ALB and ECS services, health-based routing, cross-region replication and warm-standby DR. Does not depend on Amazon Bedrock being available.
Important
CapabilitiesPipeline execution, security scanning and policy gates, and Jira, GitHub and Jenkins synchronization.
Behavior under failureRetries with back-off, queued asynchronous jobs and idempotent webhooks. If an integration is unavailable, work resumes when it recovers and teams fall back to manual workflows.
Degradable
CapabilitiesAI agent generation (requirements, architecture recommendations, code and tests) and knowledge-base retrieval.
Behavior under failureAmazon Bedrock quotas, caching and scoped context. If unavailable, jobs are queued and engineers continue manually; generated output is always reviewed by engineers before use.
Numeric availability targets are to be agreed with Clavis Technologies and validated through load, dependency-failure and failover testing; no availability results are claimed.
Network resilience against attacks, surges and lost connectivity
Only the Application Load Balancer is exposed to the internet; the application, AI/ML & data and CI/CD & security tiers sit in private subnets, with VPC endpoints keeping traffic to Amazon S3, Amazon Bedrock and Amazon ECR on the AWS network. The design addresses four scenarios:
| Scenario | How the design responds | Key services |
|---|---|---|
| DDoS attacks | Infrastructure-layer attacks are absorbed at the AWS edge and application-layer attacks are filtered by WAF rules before they reach the services. A DDoS runbook defines detection, escalation and mitigation. | AWS Shield StandardAWS WAFAmazon Route 53Amazon CloudWatchAmazon SNS |
| Unexpected traffic increase | ECS services scale out independently and the load balancer spreads requests across Availability Zones. WAF rate-based rules limit runaway clients, and long-running work is queued as asynchronous jobs. | Amazon ECS with AWS FargateApplication Load BalancerAWS WAFVPC endpoints |
| Loss of connectivity | Multi-AZ design means the loss of one zone does not interrupt service. Health checks remove unhealthy targets and, on a regional disruption, redirect traffic to the DR region. Calls to integrations retry and degrade gracefully. | Multi-AZ VPCALB health checksRoute 53 health-check failover |
| Connectivity removed by misconfiguration | Network resources are version-controlled and promoted through Dev, Test and Prod, so a change can be rolled back to the last known-good version. Drift is evaluated continuously and every change is audited. | AWS CloudFormation or TerraformAWS ConfigAWS CloudTrail |
Limiting traffic growth
The platform also limits how much traffic it generates: integrations use webhooks with idempotency keys instead of polling, frequently used knowledge-base context is cached, agent context is scoped to the tenant and project, responses are paginated and incremental, and per-tenant throttling with exponential back-off and circuit breakers prevents retry storms. Because of this, the DR region can take full production load without needing disproportionate capacity.
Rehearsed response
When connectivity is lost, the platform team follows an eight-step runbook — detect, notify, scope, contain, remediate misconfiguration, fail over, validate and review — backed by scenario playbooks for the loss of an Availability Zone, an integration, Amazon Bedrock, DNS or the edge layer, and a whole region. The procedure is exercised in the periodic failover drills.
A warm-standby DR region, ready when the primary region is not
The platform is deployed multi-AZ in a primary (active) region, with a second region held as a warm standby. Within each region, multi-AZ compute and data stores absorb single-AZ failures automatically. Across regions, data and configuration are continuously replicated so the DR environment mirrors production.
Cross-region replication
Amazon S3 cross-region replication protects artifacts, ADRs, SBOMs, logs and reports; Amazon ECR replication keeps container images available; the vector database is replicated or restored from snapshots; AWS Secrets Manager replicas keep credentials available.
Health-check failover
Amazon Route 53 health-check-based failover redirects dashboard, API, webhook and integration traffic to the DR region.
Warm standby
The DR region keeps a minimal-scale Amazon ECS (AWS Fargate) environment; on failover the services are scaled out, and the vector database is activated. Amazon Bedrock model access is enabled in the DR region in advance.
Deliberate, runbook-driven failover
The platform team is alerted through Amazon CloudWatch alarms and Amazon SNS, and runbooks cover regional failover, data-store restoration and re-running of in-flight agent tasks.
Consistent redeployment
ECS task definitions, infrastructure templates and Jenkins pipeline definitions held in GitHub allow the environment to be redeployed consistently, with AWS KMS-managed encryption applied in both regions.
Tested recovery
Non-disruptive failover drills run periodically in an isolated environment to validate the runbooks and the recovery targets without affecting live delivery workflows.
Target restoration of platform operations in the DR region after a regional disruption.
Target maximum data-loss window, based on cross-region replication for Amazon S3, Amazon ECR and the vector database.
RTO and RPO values are proposed operational targets and should be validated against the agreed Clavis Technologies DR/SLA requirements.
What changes for engineering teams, day to day
A shorter path from requirement to release
Linked stories, ADRs, code and tests are generated from a shared context, reducing the rework that comes from requirements and implementation drifting apart.
Less repetitive work
Agents take on stories, boilerplate code, unit tests and pipeline setup, leaving engineers to review, refine and decide.
Security built in from design
Security requirements, SAST/DAST gates and SBOM generation run from requirement to release, so issues are found earlier instead of becoming a release bottleneck.
Less dependence on senior engineers
Architecture recommendations draw on a knowledge base of Clavis Technologies’ standards and past decisions, supporting faster developer onboarding.
One view of delivery
Jira, GitHub, Jenkins and security tools are coordinated by the agent layer and surfaced in a single dashboard with delivery analytics.
Audit-ready governance
Multi-tenant RBAC, audit logs and recorded agent actions let any release be traced to who requested it, what the agents produced, what was reviewed and which gates it passed.
Engineers stay accountable
Human review and approval remain mandatory for every generated artifact; nothing is merged or released automatically.
Resilient by design
The dashboard, audit trail and existing artifacts stay available even if Amazon Bedrock or an integration is unavailable, and the platform is protected against DDoS attacks, traffic surges and misconfiguration.
Cost visibility
Cost allocation by tenant, environment and workflow stage, with budgets and anomaly alerts, keeps spend accountable as usage grows.
Room to scale
Amazon ECS with AWS Fargate scales each service independently, Amazon Bedrock scales with concurrent agent requests, and multi-AZ plus cross-region DR supports growth in teams and projects.
Clavis Technologies’ expected gains
Development lifecycle time
Before the platform, requirement analysis, design and build were performed manually across fragmented tools, and each hand-off added delay and rework. Clavis Technologies targets a 30% reduction in overall development lifecycle time, with requirement-to-release cycle time measured through delivery analytics.
Repetitive development tasks
Stories, boilerplate code, unit tests and pipeline setup were previously done by hand. Clavis Technologies targets 60% of repetitive development tasks completed by agents, freeing engineers to review, refine and make design decisions.
Manual documentation effort
Stories, Architecture Decision Records and reports were written manually. Clavis Technologies targets a 40% reduction in manual documentation effort, as agents generate and maintain these artifacts.
CI/CD pipeline setup
Pipelines were assembled separately for each project. Clavis Technologies targets a 35% reduction in pipeline setup time, measured from project start to the first successful pipeline run.
Release quality and security compliance
Security issues were previously found late in the cycle, causing rework and release bottlenecks. With policy-based SAST/DAST gates and SBOM generation on every build, issues are expected to be detected earlier. This improvement will be measured through gate pass rates and SBOM coverage.
Developer onboarding and traceability
Architecture decisions depended on a few senior engineers. With recommendations grounded in the organization’s own standards and past decisions, onboarding is expected to be faster and every release traceable from request to approval. This will be measured by the time to the first merged change and by linked story-to-release records.
Platform availability
Availability was not systematically tracked. The multi-AZ design, tiered availability and cross-region disaster recovery are expected to deliver high availability, monitored through Amazon CloudWatch uptime metrics and periodic failover drills against the RTO and RPO targets. Numeric availability targets are to be agreed and validated through load and failover testing.
Cost visibility
Usage-based costs for model inference, containers and storage could not previously be attributed to tenants or workflow stages. With cost allocation tags, AWS Budgets alerts and AWS Cost Anomaly Detection, spend is expected to become attributable and forecastable. Quantified baselines will be validated against AWS Cost Explorer and delivery data.
Clavis Technologies expects the platform to shorten the path from requirement to release by 30%, automate 60% of repetitive development work, and reduce documentation effort by 40% and pipeline setup time by 35%. At the same time, it expects security issues to be caught earlier, each release to be fully traceable and the platform to stay available through attacks, traffic surges and regional disruption, while engineers keep control of every approval. Together, these outcomes represent the return on investment in the platform.
Figures are target outcomes and are to be validated against measured results from delivery analytics, CloudWatch and gate reports. RTO and RPO values are proposed operational targets and should be validated against the agreed Clavis Technologies DR/SLA requirements.
