Engagements
Agent delivery engagements. Outcomes, not seats.
Bring us a defined project, a recurring backlog, or a Java program that needs additional delivery capacity. We combine principal architecture with context grounded agents and take responsibility for reviewed production output.
Flagship engagement
Embedded Java Agent Delivery Pod
A principal led Java delivery function that works inside your planning rhythm, owns agreed workstreams, and delivers code, tests, documentation, and cloud changes. Available for 6 to 12 month corp to corp engagements.
What the pod includes
Client specific context layer
a context layer built over your repositories and exposed to our agents through MCP, so agents work from verified information about the estate before they write code, rather than guessing from isolated files.
Managed agent workflows
agents execute bounded workstreams in parallel under defined guardrails, each one scoped, sequenced, and supervised. Agent assisted, with a person accountable for every outcome.
Java and AWS architecture
service design, upgrade sequencing, and cloud decisions made at principal level, so agent output lands inside a structure someone actually chose and can defend.
Implementation and review
working software delivered against agreed workstreams, with human review of production code before it merges and cloud changes applied through your existing controls.
Testing and documentation
every change lands with tests and updated documentation, so the state of main is always explainable and always ready to release.
Release, handoff, and reporting
release and handoff support through your change process, plus standing reporting on progress, risk, and blockers so you always know where each workstream stands.
Technologies
Java 8 / 11 / 17 / 21 · Spring Boot · Spring Framework 6 · Spring Security · Jakarta EE 10 · OpenRewrite · AWS Transform · Amazon Q Developer · Maven · Gradle · JUnit · Testcontainers · Docker · ECS / EKS · AWS IAM · Infrastructure as code · CI/CD · JVM tuning
The differentiator
Ground truth before generated code.
We do not drop an agent into a repository and ask it to guess. We build a client specific context layer, exposed to our agents through MCP, so they work from verified information about the codebase rather than isolated files.
What the layer covers
Repositories and module maps
which repositories exist, how modules relate, and where each part of the estate lives, so agents navigate real structure instead of inferring it from file contents.
Dependencies and interfaces
the dependency graph and the interfaces between services, so a question like what depends on this module is answered from verified data rather than assumption.
Build systems, tests, and standards
how the code builds, how it is tested, and the conventions it must follow, so generated changes match the way your teams actually work.
Cloud environment and delivery constraints
the AWS footprint, environments, and release constraints that define what a safe change looks like before anything moves toward production.
Technologies
MCP · Python · Tool schema design · Claude Code · Java · Spring Boot · Maven · Gradle · OpenTelemetry · Splunk · AWS IAM · VPC deployment · Audit logging
Fixed scope
A defined outcome, agent accelerated, principal reviewed.
A bounded Java or Java cloud outcome with acceptance criteria, a delivery plan, reviewed code, tests, documentation, and deployment or handoff support.
Example outcomes
A new service or API
a Spring Boot service or API designed, implemented, tested, and documented from an agreed specification through to deployment or handoff.
A framework upgrade
a Java, Spring, or dependency upgrade planned and executed against acceptance criteria, with test coverage established before the change rather than after it.
An integration
a defined integration between systems, with typed contracts, explicit error handling, and tests that prove the boundary behaves under failure.
A cloud deployment
a workload deployed onto AWS with infrastructure as code, CI/CD, and the operational checks that make the deployment repeatable.
A reliability initiative
a scoped effort against a known problem area such as performance, stability, or test coverage, delivered as reviewed, tested changes.
Technologies
Python 3.x · FastAPI · PyTorch · scikit-learn · Pandas · NumPy · Batching / async serving · Multi worker execution · Model deployment · Schema validation · Grafana · PostgreSQL / MS SQL Server
Every engagement
Not a seat, not a subscription.
This is not a resume placed into an open seat, and it is not an autonomous agent subscription. We take responsibility for a scoped delivery function and work through your architecture, product, security, and release controls.
What every engagement includes
Principal level technical ownership
architecture, security judgment, code review, and production readiness sit with the principal architect, who answers during cutover.
Context grounded in your actual systems
agents work from a context layer built over your repositories, so output reflects the estate as it really is rather than as a model imagines it.
Managed agent execution
agents accelerate bounded workstreams while every line of production code is human reviewed before it ships.
Testing, documentation, and knowledge transfer
delivery evidence you keep: tested changes, current documentation, and handoff sessions so your engineers can own the result.
Transparent progress and risk reporting
standing reporting on progress, risk, and blockers, with production approvals always under client control.
Technologies
Java · Spring Boot · Python · FastAPI · gRPC · REST · Kafka · Solace · PyTorch · Schema versioning · Latency profiling
Start here
The entry engagement
Assessment and Agent Launch
Two weeks · fixed scope
A focused first engagement that maps the relevant estate, identifies blockers, defines agent ready workstreams, and produces an actionable delivery sequence.
- Week one, map the estate. Repositories, modules, dependencies, blockers, and test coverage reality.
- Week two, the launch plan. Architecture observations, a risk and blocker register, the recommended first workstream, and a running context layer you keep.
Stack
Languages
Java (8, 11, 17, 21), Python, C++, SQL
Java / JVM
Spring Boot, Spring Framework, Spring Security, Spring Cloud, Spring Batch, Spring Data, Jakarta EE, Maven, Gradle, JUnit, Mockito, Testcontainers, JVM tuning, thread and heap dump analysis, multithreading and concurrency
Python & ML
Python 3.x, FastAPI, PyTorch, scikit-learn, Pandas, NumPy, production inference platforms, batching and asynchronous serving, multi worker execution, model packaging and deployment, schema validation at scale
MCP & agents
MCP server design in Python, tool schema modeling, Claude Code integration, agent orchestration with deterministic computation delegated to production services
Integration
gRPC and REST contracts, Kafka, Solace, schema versioning across JVM and Python runtimes, latency profiling at service boundaries
Migration tooling
OpenRewrite, AWS Transform, Amazon Q Developer, Moderne, repository scale dependency analysis
Cloud & infra
AWS (EC2, ECS, EKS, S3, Athena, IAM), Docker, Linux (Red Hat), Azure DevOps, Git, CI/CD pipelines
Observability
OpenTelemetry, Splunk, Grafana, Micrometer, structured logging, distributed tracing, metrics and alerting
Data & messaging
PostgreSQL, MS SQL Server, Kafka, Solace, REST APIs, high throughput message processing
Contact
Discuss the engagement
Bring us the workstream, the backlog, or the program. We will propose the smallest valuable first step.