CoderFlow: Agentic Coding for Complex Enterprise Systems
Autonomous AI that executes the full engineering workflow
AI coding assistants generate better code and help developers work more efficiently, but they still require engineers to orchestrate every step: planning the approach, running builds, troubleshooting failures, and validating results.
CoderFlow handles the entire development cycle autonomously, turning AI assistance into complete, production-ready outcomes. It runs multiple AI models simultaneously—Gemini, Claude, and Codex, etc, and chooses the best performer for each task. Autonomous agents handle the complete cycle: compiling, testing, validating, and fixing code in isolated containers separate from production.
Your developers orchestrate the work and review verified, production-ready outcomes instead of debugging AI fragments. The result: faster development processes for complex, legacy systems where expertise is scarce.
Why Teams Using “AI Coding” Still Fall Behind
How AI Works Today:
Designed for clean, modern code
Suggests code
Operates outside production environments
Leaves testing and risk to developers
Delivers incremental gains
What Enterprises Need:
Designed for complex enterprise systems
Executes and validates code
Runs inside enterprise infrastructure
Automates testing, validation, and safeguards
Enables transformational productivity
What Changes When AI Executes Instead of Assists
CoderFlow replaces the human-driven execution loop with autonomous, orchestrated execution.
Instead of asking AI for help, engineers delegate work.
CoderFlow:
Breaks objectives into executable steps
Runs work in parallel across multiple agents
Compiles, tests, validates, and fixes failures
Uses judge agents to evaluate correctness
Produces ready-to-review outcomes, not suggestions
Developers stop doing the work, and start orchestrating it.
What Makes CoderFlow Different?
CoderFlow is not just an AI coding assistant. It is more than an IDE plugin, or a chat interface, or a suggestion engine.
CoderFlow runs autonomous agents that finish engineering work inside your infrastructure.
The Difference:
Define and Enforce “Working Code”
Agents evaluate their own output using concrete signals—successful builds, tests, and expected runtime behavior—not just generated text.
Execute Full Build–Test–Fix Loops Until Validation Succeeds
Compile, test, detect failure, retry, and converge autonomously—reducing dependence on constant human prompting.
Operate Across Multiple Repositories and Systems
Designed for real enterprise environments, not single-repo, greenfield projects.
Explore and Compare Solutions in Parallel
Multiple agents can propose alternatives, with automated evaluation explaining tradeoffs and selecting validated results.
Run Autonomously, Escalate Only When Judgment is Required
Humans review verified outcomes in controlled environments instead of driving every step of the AI.
Developers orchestrate objectives and approve results—shifting their role from writing and debugging code to reviewing ready-to-commit, validated outcomes. If assistants help you write code, CoderFlow helps you get the work to the finish line.
Why CoderFlow Delivers Real Productivity Gains
Runs Inside Your Environment
On-Premise or Private Cloud Deployment
All Execution Stays Local
Only Minimal Context is Sent to Cloud LLMs
No Full Repos, Credentials, or Data Leave Your Network
Autonomous, With Governance
Full build → Test → Fix → Retest Cycles
Developer Approval as a Control Point
Full Logs, Audit Trails, and Visibility
Built for Enterprise & IBM i
Multi-Repository Orchestration
IBM i, RPG, COBOL, 5250, Rich Display Files
Modern Stacks: Node.js, Java, Python, .NET
Git, JIRA, CI/CD Integration
Parallel, Multi-Model Execution
By running multiple models simultaneously and automatically comparing their outputs, you can select the best validated result.
Finished Work, Not Suggestions
While AI coding assistants can generate code, CoderFlow delivers verified results that are ready to commit.
What You Get:
Containerized agentic execution platform
Parallel agent orchestration with model comparison
IBM i–AI tools for building, executing and testing native
Templates for conversion, refactoring, and documentation
Automated UI and regression testing
CI/CD and Git-based approval workflows
All delivered as a persistent, governed enterprise platform—not an ephemeral browser session.
Security and Governance
Enterprise-Grade Security Without Compromise
Runs Inside Your Infrastructure
On-premise or private cloud deployment. All execution—builds, tests, validation, and Git operations—stays inside your network.
Minimal Context to Cloud Models
Only the necessary code snippets are sent to cloud models (Claude, OpenAI, Gemini). No full repositories, credentials, or bulk data leave your environment.
No Direct Model Access
Agents act as secure intermediary. Cloud models never touch your IBM i, repos, databases, or servers. Execution remains local, with only minimal context passed to models.
Isolated Container Execution
Least-privilege access, isolated execution, and fully auditable workflows.
Customer-Controlled Credentials
All secrets, service accounts, and Git credentials stay local and are never exposed to LLMs.
Built-In Governance
Role-based access controls, complete audit trails, standardized compliance templates, and developer approval gates.
The Solution
Built for Teams Futurizing Real Systems
CoderFlow Helps Enterprises:
Futurize without disruption
Reduce backlog and technical debt
Prepare for developer retirement
Deliver faster—without increasing risk
Who CoderFlow Is For:
Teams maintaining large, long-lived systems
Backlogs constrained by testing, validation, and risk
Environments where mistakes are expensive
Learn More About Agentic Coding with CoderFlow
Agentic coding shifts development from writing code to orchestrating autonomous agents and reviewing their verified outcomes.