DEV Community: Abhay Rao

HowTo: AI Pair Programming with IBM Bob

Abhay Rao — Thu, 15 Oct 2026 05:18:30 +0000

AI pair programming becomes much more valuable when it goes beyond asking an AI assistant to generate an entire application from a single prompt. A better approach is to treat AI as a development partner that works alongside you throughout the engineering process, from understanding requirements and planning the solution to implementation, testing, refactoring, and documentation.

That's the approach explored in this AI Pair Programming with IBM Bob tutorial. Instead of relying on one-shot code generation, the tutorial demonstrates a structured workflow for building a FastAPI To-Do API while keeping the developer involved in important decisions and validation.

Start with Requirements, Not Code

Before generating anything, the workflow begins by defining what the application should actually do. Clear requirements give Bob the context needed to understand the intended functionality instead of making assumptions about the solution.

This is an important shift in AI-assisted development. The goal isn't to write the longest prompt possible. The goal is to give the AI enough relevant context to understand the problem and expected outcome.

Plan Before You Implement

Once the requirements are established, Plan mode can be used to turn them into a structured implementation approach.

This gives the developer an opportunity to review the proposed direction before code is generated. Missing requirements, assumptions, or architectural decisions can be identified and refined while they are still part of the plan rather than after they have become implementation.

AI makes implementation faster, but that makes good planning even more valuable.

Move from Plan to Working Code

After reviewing the plan, Agent mode can help translate that plan into an implementation.

In the tutorial, this process is used to build the FastAPI application. But generated code isn't treated as the final result. The developer remains part of the loop by reviewing the implementation and validating whether the application matches the original requirements.

The workflow looks like this:

Developer defines intent
        ↓
Bob creates a plan
        ↓
Developer reviews decisions
        ↓
Bob implements
        ↓
Developer and Bob validate

That's much closer to pair programming than simply asking an AI tool to "build this for me."

Keep the Context Focused

Another important lesson from the tutorial is context management.

Long AI conversations can accumulate requirements, experiments, failed approaches, code snippets, and unrelated information. Eventually, all of that context can begin influencing later work.

Using fresh context windows for focused stages of development helps keep the implementation aligned with the current objective.

For larger projects, this becomes increasingly important. Good AI pair programming isn't only about providing more context. It's about providing the right context at the right time.

Refactor Instead of Stopping at "It Works"

Getting an application to run is only one stage of software engineering.

Once the initial implementation is working, IBM Bob can assist with reviewing and refactoring the code to improve its structure and overall quality.

AI-generated code shouldn't automatically be considered finished simply because it executes successfully. Just like human-written software, it should be reviewed, improved, tested, and validated before being treated as complete.

Generate Tests and Validate the Application

Testing becomes another part of the collaborative workflow.

The tutorial demonstrates using Bob to generate unit tests and validate application behavior. Rather than separating testing from implementation, testing becomes part of the same development cycle.

The workflow therefore evolves naturally from generation into verification:

Generate → Review → Refactor → Test → Validate

This helps shift AI-assisted development away from pure code generation and toward complete engineering outcomes.

Document What You Built

Documentation is frequently postponed until the end of a project or skipped entirely.

With an AI pair-programming workflow, documentation can become part of the development process itself. After the implementation stabilizes, Bob can help create technical documentation based on the completed work.

That means future developers receive more than a repository full of code. They also gain context around what was built and how the application is structured.

Take the Application to Docker

The tutorial completes the journey by building and running the application with Docker.

This demonstrates that AI pair programming doesn't need to end when the source code has been generated. The workflow can continue through testing, documentation, packaging, and running the application.

The Complete AI Pair-Programming Workflow

Requirements
    ↓
Plan
    ↓
Review
    ↓
Generate Code
    ↓
Validate
    ↓
Refactor
    ↓
Test
    ↓
Document

What I like about this workflow is its structure.

The developer doesn't disappear from the process, and Bob isn't treated as a one-click code generator. Instead, both contribute differently. The developer provides requirements, intent, judgment, feedback, and validation, while Bob helps accelerate the implementation-heavy parts of the process.

That's ultimately what makes AI pair programming different from simple AI code generation.

The most effective workflow isn't necessarily the one that generates the most code from the fewest prompts. It's the one that helps you move from an idea to a well-planned, reviewed, tested, and documented implementation while staying in control of the engineering decisions.

Learn More

📖 Follow the complete tutorial:

AI Pair Programming with IBM Bob

⤵️ Use IBM Bob for free and explore the workflow yourself:

Get started with IBM Bob

Understanding Before Modernizing: The First Step Most Enterprises Can't Skip

Abhay Rao — Tue, 06 Oct 2026 18:09:38 +0000

When people talk about modernization, the conversation usually starts with new architectures, cloud migrations, AI, microservices, or platform upgrades.

In reality, most modernization efforts begin with a much simpler question:

Can anyone actually understand this system?

Across many organizations, critical applications have been running for decades. They contain years of accumulated business logic, custom integrations, workarounds, and operational knowledge that may never have been formally documented.

Over time, documentation becomes outdated, original architects move to different roles, and institutional knowledge becomes concentrated in a small group of individuals. Eventually, teams face a situation where maintaining the system is difficult enough, let alone modernizing it.

That's where modernization often stalls before it even starts.

Understanding Is the Real First Milestone

Before migrating platforms, refactoring code, upgrading frameworks, or redesigning architectures, teams need answers to fundamental questions:

What does this application actually do?
Which systems depend on it?
What business processes does it support?
Which components can be modified safely?
How will a change affect surrounding applications?

Without that understanding, modernization becomes guesswork.

The challenge isn't necessarily rewriting the code.

The challenge is understanding the existing system well enough to make informed decisions.

Making Legacy Systems Tractable

What is changing today is the ability to accelerate that understanding.

Instead of spending weeks manually tracing dependencies, reading unfamiliar code, and piecing together fragmented documentation, development teams can use AI-assisted workflows to quickly build a picture of how applications work.

Capabilities such as:

✅ Explaining complex files and code structures
✅ Surfacing application dependencies and system relationships
✅ Generating usable documentation from existing codebases
✅ Mapping architecture and application flows
✅ Identifying modernization opportunities and risks

can dramatically reduce the time required to move from uncertainty to action. The result isn't simply better documentation. The result is faster decision-making.

Why Clarity Matters

Modernization projects succeed when teams can confidently understand the systems they're changing.

When developers can see dependencies clearly, understand implementation patterns, and access meaningful documentation, planning becomes easier, risk becomes easier to manage, and modernization initiatives can move forward with greater confidence.

That early clarity often creates a ripple effect across the entire software delivery lifecycle.

The Next Question

Once understanding is no longer the bottleneck, a new question naturally emerges:

Now that we understand the system, how fast can we move?

That is where modern agentic development workflows become particularly powerful.

Modernization doesn't start with transformation.

It starts with understanding.

And understanding creates the foundation for everything that follows.

⛓️‍💥 Try IBM Bob and explore modern software understanding and modernization workflows:

👉 Start your IBM Bob trial

Bobathon: Building Solutions for Bengaluru with IBM Bob

Abhay Rao — Tue, 06 Oct 2026 18:04:26 +0000

Technology is at its best when it solves problems people experience every day.

That's the idea behind IBM Bobathon, a hackathon focused on turning local challenges into practical, usable solutions for Bengaluru. Rather than building projects in isolation, participants are encouraged to identify real problems, collaborate with others, and create solutions that can make a tangible impact on communities using IBM Bob as their development partner.

What makes Bobathon particularly interesting is its focus on outcomes rather than technology alone. Participants aren't just asked to build something innovative. They're challenged to create something useful, relevant, and meaningful for the people who live and work in Bengaluru.

Solving Real Problems, Not Hypothetical Ones

Cities face countless challenges that affect daily life. From mobility and public services to accessibility, employment opportunities, and community engagement, there are many areas where technology can make a difference.

Bobathon encourages participants to tackle these issues through four challenge tracks:

1. Agentic AI for Civic Challenges

How can agentic AI help solve Bengaluru's everyday challenges?

This track focuses on applying AI to improve how citizens interact with services, access information, and navigate urban environments. Participants can explore ideas that improve efficiency, accessibility, and responsiveness across city systems.

2. Expanding Employment and Community Impact

Technology has the potential to connect people with opportunities and help community initiatives reach larger audiences.

This track focuses on solutions that improve access to employment, skill development, volunteering opportunities, and community programs.

3. Building Safer and More Inclusive Communities

A city works best when everyone can participate.

Participants are encouraged to create solutions that improve safety, accessibility, and inclusion for:

Children
Women
Senior citizens
People with disabilities

The goal is to use technology to help make everyday life easier, safer, and more accessible.

4. Student Innovation Track

Students are encouraged to start with a problem they personally see and experience.

Instead of working from predefined challenges, participants can identify issues affecting their school, neighborhood, campus, or community and develop solutions that address those needs directly.

Build with IBM Bob

Throughout the event, participants can use IBM Bob to help accelerate development, explore ideas, prototype solutions, and move from concept to implementation faster.

Whether it's designing architectures, generating code, planning workflows, validating solutions, or modernizing existing implementations, Bob can help builders focus more on solving problems and less on repetitive development tasks.

Exciting Prizes

In addition to the opportunity to create meaningful solutions, participants can compete for a range of prizes:

🏆 1st Prize – iPads

🥈 2nd Prize – AirPods

🥉 3rd Prize – Logitech Mice

⭐ Student Innovation Award – JBL Speakers

But beyond the prizes, the real opportunity is building something that could positively impact how people experience life in Bengaluru.

Why Events Like Bobathon Matter

Hackathons are often associated with rapid prototyping and experimentation. What makes Bobathon unique is its focus on local relevance.

The challenge isn't simply to build the most technically impressive application.

The challenge is to build something Bengaluru can actually feel.

Whether that's reducing friction in daily life, supporting underserved communities, improving access to opportunities, or creating safer experiences, the emphasis is on practical impact.

As agentic AI tools become more capable, events like Bobathon create opportunities for developers, students, designers, entrepreneurs, and problem-solvers to combine technology with community-driven innovation.

Get Involved

If you have an idea, a problem you want to solve, or simply want to explore what can be built with agentic AI, Bobathon offers a great opportunity to collaborate with others and turn concepts into working solutions.

🚀 Participate in Bobathon:
👉 Register Here

🤖 Learn more about IBM Bob:
👉 Explore IBM Bob

Why IBM Chose NVIDIA Nemotron for IBM Bob Self-Hosted

Abhay Rao — Tue, 06 Oct 2026 17:58:09 +0000

As enterprises adopt AI for software development, one challenge keeps surfacing:

How do you deliver frontier-level coding performance while keeping code, intellectual property, and development context inside enterprise-controlled environments?

That question became especially important as IBM expanded IBM Bob into self-hosted deployments. While cloud-based AI services can provide powerful capabilities, many organizations operate under strict security, compliance, sovereignty, and operational requirements that make sending sensitive code to external environments impractical.

To address this, IBM needed models capable of delivering enterprise-grade agentic development while running efficiently on customer-managed infrastructure.

After extensive evaluation, IBM selected NVIDIA Nemotron and Poolside Laguna as key supported models for IBM Bob self-hosted deployments.

The Challenge: Bringing Frontier AI On-Premises

Self-hosted AI creates a very different set of requirements compared to public SaaS deployments.

Organizations operating in regulated industries often need:

Source code to remain inside approved environments
Development artifacts to stay within enterprise boundaries
Air-gapped deployment options
Flexible infrastructure control
Predictable performance and latency
Governance over model usage

At the same time, developers still expect modern AI capabilities.

The solution cannot simply be secure.

It also needs to be effective.

That balance became the driving factor behind the model evaluation process.

How IBM Evaluated Candidate Models

Rather than choosing models based solely on benchmark scores, the IBM Bob team evaluated both open-weight and closed-weight models across several dimensions that matter in real enterprise environments.

1. Hardware Footprint

Many frontier models require enormous computational resources.

While those models may produce excellent results, deploying them on-premises can become prohibitively expensive.

IBM evaluated models across different size profiles to determine which models provided the best balance between intelligence and infrastructure requirements.

The goal was practical deployment, not simply maximal model size.

2. Coding Accuracy

A coding model ultimately needs to help solve software engineering problems.

IBM benchmarked candidate models across real-world development workflows, including:

Simple development tasks
Medium-complexity engineering work
Multi-step software modernization scenarios
Agent-assisted workflows

The emphasis was on actual task completion rather than isolated benchmark scores.

3. Latency

Developer productivity depends heavily on feedback loops.

When an agent performs analysis, tool calls, file operations, and execution workflows, slow responses create friction.

IBM therefore measured:

Time-to-first-token
Overall response latency
Generation throughput
Multi-step workflow responsiveness

4. Openness

A self-hosted deployment requires more than raw capability.

Organizations need models they can govern, deploy, and operate within their own environments.

This makes open-weight models especially attractive for customer-managed deployments, including highly controlled and air-gapped environments.

Looking Beyond Raw Model Benchmarks

One particularly interesting aspect of the evaluation process was that IBM did not evaluate models in isolation.

The Bob team evaluated models together with the surrounding agent harness.

This matters because agentic development extends beyond model reasoning.

Real-world workflows involve:

Tool calling
Terminal interactions
Planning systems
Skills
Modes
Agent orchestration

A model that performs well in benchmarks may not necessarily perform well inside a complete software engineering workflow.

The evaluation therefore focused on end-to-end performance.

Co-Engineering with NVIDIA

During early testing, NVIDIA Nemotron models demonstrated strong reasoning capabilities.

However, IBM identified opportunities to improve their effectiveness within agent-based software development workflows.

The IBM Bob engineering team collaborated closely with NVIDIA to refine the behavior of the models within Bob's environment.

Optimization efforts included:

Prompt tuning
Sampling adjustments
Reasoning configuration improvements
Agent harness modifications

The process wasn't simply about adapting Bob to the model.

It was about co-engineering the complete experience.

Feedback from IBM engineers and internal Bob users was continuously incorporated into the evaluation and optimization process.

What IBM Found

NVIDIA Nemotron 3 Ultra

NVIDIA Nemotron 3 Ultra stood out in several areas.

IBM highlighted:

Reliable and predictable output behavior
Strong adherence to tool schemas
Reduced tendency to drift off-task
Clean implementations without unnecessary complexity
Low-latency execution characteristics

According to IBM's internal benchmarks, Nemotron delivered significantly lower latency on NVIDIA Blackwell infrastructure compared with leading SaaS-based model deployments accessed over the network.

For enterprise development teams, lower latency can translate directly into faster feedback cycles and smoother agent interactions.

Poolside Laguna S 2.1

Poolside Laguna impressed for different reasons.

IBM highlighted:

Strong reasoning relative to model size
Fast response generation
Clear explanations of reasoning processes
Excellent performance-to-footprint ratio

This makes Laguna particularly attractive in situations where infrastructure resources are more constrained but high-quality coding assistance remains necessary.

What This Means for Enterprise Customers

For organizations considering self-hosted AI-assisted software development, the outcome of this work is significant.

With IBM Bob self-hosted, organizations can deploy supported models directly on their own infrastructure and connect them through Bob's model gateway.

The practical benefits include:

Code stays within enterprise-controlled environments
Development context remains local
Intellectual property remains protected
Air-gapped deployments become possible
Enterprises retain infrastructure control
Development teams gain access to agentic workflows

Perhaps more importantly, enterprises no longer need to choose between strong AI capabilities and deployment flexibility.

The combination of IBM Bob with models such as NVIDIA Nemotron and Poolside Laguna aims to make frontier-grade software engineering assistance available inside regulated and security-sensitive environments.

Looking Ahead

IBM also outlined future plans to expand model flexibility further.

One particularly interesting direction is multi-model routing.

As enterprises adopt multiple models for different purposes, routing workloads to the most appropriate model could help balance:

Cost
Performance
Accuracy
Infrastructure utilization

This aligns with a broader industry trend where organizations increasingly use multiple models rather than relying on a single AI provider.

Final Thoughts

The most interesting aspect of this announcement is that it shifts the conversation away from model rankings and toward deployment reality.

Many organizations already know AI can improve software development.

The challenge is bringing those capabilities into environments where their most important applications live.

IBM's decision to support NVIDIA Nemotron and Poolside Laguna reflects a practical enterprise requirement:

Deliver strong agentic development capabilities while preserving control over infrastructure, code, data, and operational environments.

For regulated industries, air-gapped environments, and enterprises managing sensitive intellectual property, that combination may be just as important as model intelligence itself.

Learn More

📖 Read the full announcement:
👉 Why IBM Chose NVIDIA Nemotron for IBM Bob Self-Hosted

⤵️ Try IBM Bob for free: 👉 Start Your IBM Bob Trial

How to Stop Vibe Coding & Build Better Apps with Spec Kit

Abhay Rao — Mon, 05 Oct 2026 06:14:39 +0000

📹 Watch the video: How to Stop Vibe Coding & Build Better Apps with Spec Kit

📂 Get the Spec Kit GitHub repository: GitHub Repo

⤵️ Try IBM Bob for free: IBM Bob

Modern AI tools can generate code quickly, but speed alone doesn't guarantee quality. In this session, Ash Minhas demonstrates how Spec-Driven Development helps teams move beyond "vibe coding" by establishing clear requirements, architecture decisions, and implementation plans before AI begins writing code.

Using Spec Kit alongside IBM Bob, developers can transform high-level ideas into structured specifications, technical plans, and executable tasks. This creates a more predictable development workflow where AI-generated code aligns with project goals, business requirements, and engineering standards.

Instead of relying on repeated prompts and hoping for the best outcome, the process focuses on defining what success looks like first, then using AI to implement against those specifications. The result is improved consistency, reduced rework, better collaboration, and software that is easier to validate and maintain.

💡 Key Topics Covered

📜 What Spec-Driven Development Is and Why It Matters

Learn why defining specifications before coding leads to more reliable AI-assisted development and better software outcomes.

🏛️ Creating a Project Constitution

Establish project principles, coding standards, constraints, and governance rules that guide all future development work.

📝 Writing Feature Specifications

Define requirements, user stories, acceptance criteria, and expected behaviors before implementation begins.

🏗️ Generating Technical Plans

Turn business requirements into structured architectural and implementation plans that AI can accurately execute.

✅ Breaking Work into Tasks

Convert specifications into organized development tasks that can be implemented incrementally and validated throughout the process.

🤖 Implementing Features with IBM Bob

Leverage IBM Bob to generate code, assist development, and execute against predefined plans instead of relying on ad-hoc prompting.

🔍 Validating Against Requirements

Verify that the final implementation satisfies the original specifications, acceptance criteria, and project objectives.

The video offers a practical look at how Spec Kit + IBM Bob can help development teams build software in a more structured, predictable, and scalable way, moving from AI-generated code toward AI-assisted software engineering.

📹 Watch: YouTube Video

📂 Repository: Spec Kit GitHub Repo

🚀 Try IBM Bob: Get Started

What Developers Are Saying About IBM Bob #14

Abhay Rao — Sun, 04 Oct 2026 18:24:15 +0000

Modernization is one of the most challenging areas in enterprise software development. Upgrading frameworks, runtimes, and architectures is rarely just a technical exercise. Teams need to understand existing applications, preserve business logic, and ensure that modernization efforts align with long-term engineering goals.

That's why this perspective from Artur Skowronski, Head of Java & Kotlin Engineering at VirtusLab, stood out to me.

"The tool works very well and captures the modernization intent. Bob is the first tool of its kind to treat Java as a first-class citizen."

What I find particularly interesting is the emphasis on modernization intent rather than code generation alone. Modernizing enterprise Java applications often involves understanding dependencies, assessing architectural trade-offs, updating frameworks, improving security posture, and ensuring compatibility across large application portfolios.

For many organizations, Java remains the foundation of mission-critical systems. Having an AI development partner that understands Java deeply and can assist throughout modernization workflows has the potential to reduce complexity while improving consistency across projects.

As organizations continue investing in Java modernization, the focus is increasingly shifting from generating code faster to confidently evolving long-lived applications. That's where purpose-built, platform-aware AI tooling can make a meaningful difference.

🔗 Try IBM Bob today: Start Free Trial

🔗 Explore flexible plans: View Pricing

Agentic Development. On Your Terms.

Abhay Rao — Sun, 04 Oct 2026 18:19:08 +0000

One of the biggest challenges enterprises face when adopting AI-assisted software development isn't capability. It's deployment.

Many organizations want to bring AI into their development workflows, but their most important applications often operate under strict security, sovereignty, compliance, and operational requirements. Source code, build artifacts, development context, and business logic frequently cannot leave approved environments.

That's why the general availability of IBM Bob's self-hosted deployment option is notable.

IBM Bob can now be deployed in supported self-hosted, air-gapped, and hybrid environments, allowing organizations to extend agentic software development to sensitive and mission-critical workloads while maintaining control of their infrastructure, deployment architecture, and supported models.

What stands out is that developers continue to get the familiar Bob experience, including:

Bob IDE workflows
Bob Shell
Agentic development capabilities
Parallel tool calling
Skills and modes
Application understanding and modernization workflows

At the same time, platform and security teams gain the flexibility to determine where model inference happens and how development data is handled.

This is particularly relevant for industries such as:

Financial services
Government
Healthcare
Critical infrastructure
Enterprises protecting sensitive intellectual property

Another interesting aspect is support for both self-hosted and hybrid model strategies. Organizations can align model selection with their security, performance, and operational requirements while building on existing investments through a Bring Your Own License (BYOL) approach.

The announcement also reinforces that IBM Bob is about more than code generation. Teams can use it to understand applications, plan modernization initiatives, execute changes, validate outcomes, and support long-term modernization efforts across Java, IBM i, and IBM Z environments through specialized Premium Packages.

The future of enterprise AI isn't just about adopting powerful models.

It's about bringing those capabilities to the software systems that organizations depend on most while maintaining security, governance, and operational control.

📖 Read the announcement:

👉 IBM Bob expands to self-hosted environments

⤵️ Try IBM Bob for free and explore agentic development:
👉 Start your IBM Bob trial

What Developers Are Saying About IBM Bob #13

Abhay Rao — Sat, 03 Oct 2026 17:17:44 +0000

One of the biggest challenges in technology today isn't building new applications. It's understanding, maintaining, and modernizing the systems that organizations have relied on for years. For teams working on IBM i environments, that often means dealing with large RPG codebases, limited documentation, and business-critical processes that cannot afford disruption.

That's why this feedback from Jasmine K., VP of Technology at M.R. Williams, stood out to me.

"M.R. Williams is advancing its IBM i modernization strategy with new mobile solutions and accounting system improvements, while leveraging IBM Bob to quickly interpret older RPG code, automate documentation, and streamline development workflows."

What I find particularly interesting is that Bob is being used across multiple stages of the modernization journey. Before teams can modernize a system, they first need to understand it. Legacy RPG applications often contain years of business rules and operational knowledge that can be difficult to uncover. By helping developers interpret older code, generate documentation, and reduce manual investigation, Bob can accelerate one of the most challenging parts of modernization.

The value extends beyond understanding legacy systems. Streamlining development workflows allows teams to spend more time delivering improvements and less time deciphering existing implementations. For organizations balancing innovation with ongoing operational requirements, that can make a meaningful difference.

As AI adoption grows, some of the most impactful use cases may come from helping organizations unlock the value hidden inside existing systems rather than starting from scratch.

⛓️‍💥 Try IBM Bob today: Start Free Trial

⛓️‍💥 Explore flexible plans: View Pricing Options

IBM Bob Expands to Self-Hosted Environments for Mission-Critical Enterprise Software

Abhay Rao — Sat, 03 Oct 2026 06:03:51 +0000

One of the biggest barriers to enterprise AI adoption has never been model capability. It has been deployment reality.

Many organizations want to apply AI to software development, modernization, and maintenance, but their most valuable applications often live in environments with strict security, compliance, and operational requirements. For industries such as financial services, healthcare, government, and critical infrastructure, source code, application logic, and development artifacts frequently cannot leave approved environments.

That's why the general availability of IBM Bob's self-hosted deployment option is such an important milestone.

Rather than forcing organizations to choose between AI adoption and infrastructure control, IBM Bob now enables enterprises to bring agentic software development into customer-managed environments while maintaining the flexibility required by sensitive workloads.

Agentic Development on Enterprise Terms

IBM Bob was built as an AI-powered development partner that helps teams understand, plan, implement, and validate software changes throughout the software development lifecycle.

With the introduction of self-hosted deployment, those capabilities can now be applied to applications that were previously excluded from AI-assisted workflows because of security or sovereignty requirements.

This means organizations can bring Bob closer to the applications that matter most while keeping infrastructure decisions aligned with internal policies and operational needs.

Developers continue to work through a familiar Bob experience, including:

IBM Bob IDE capabilities
Bob Shell
Skills and modes
Agent harness workflows
Parallel tool calling
Development and modernization workflows

At the same time, platform and security teams gain greater control over how deployment and model inference are managed across different workload categories.

Flexible Deployment for Different Risk Profiles

A major strength of the self-hosted approach is flexibility.

Enterprise software portfolios rarely have identical requirements across all applications. Some workloads require strict isolation, while others can safely leverage external model services.

IBM Bob supports multiple deployment approaches, including:

Self-hosted deployments
Air-gapped environments
Hybrid configurations

This allows organizations to align deployment choices with individual workload requirements.

For example, an organization could choose on-premises model inference for highly sensitive systems while using approved external model services for lower-risk development projects. Developers receive a consistent user experience, while security and compliance teams retain architectural control.

That flexibility is especially important for enterprises handling:

Sensitive intellectual property
Proprietary business logic
Personal data
Regulated workloads
Mission-critical systems

Expanding Model Choice with BYOL

Another notable capability is support for customer-managed model strategies. Organizations can connect IBM Bob to supported models based on performance, infrastructure, and deployment requirements.

Supported deployment approaches include:

Self-Hosted Models

Organizations manage the models directly within their infrastructure.

Examples include:

NVIDIA Nemotron
Poolside Laguna

Hybrid and Private SaaS Models

Organizations can connect IBM Bob to approved external model services using supported connectivity patterns.

Examples include:

Claude Sonnet
Claude Opus
Gemini
OpenAI GPT models

An additional advantage is the Bring Your Own License (BYOL) model, allowing organizations to leverage existing model investments while integrating those models into IBM Bob workflows.

This provides flexibility around:

Cost optimization
Infrastructure utilization
Performance trade-offs
Existing AI investments

Looking ahead, IBM has also outlined plans for multi-model routing, enabling organizations to automatically align tasks with the most appropriate model based on workload characteristics.

Supporting Enterprise Modernization Efforts

The value of IBM Bob extends beyond code generation.

Many organizations operate application portfolios that have evolved over decades. Before modernization begins, teams need to understand:

Existing business logic
Application dependencies
Platform integrations
Operational impacts
Migration risks

IBM Bob is designed to help with that broader challenge.Rather than serving purely as a code-generation engine, Bob assists with:

Application understanding
Change planning
Implementation workflows
Validation activities
Modernization initiatives

With self-hosted deployment, these capabilities can now be applied to applications that must remain within approved enterprise environments.

Premium Packages for Platform Modernization

Organizations can further extend IBM Bob through platform-specific Premium Packages.

IBM Bob Premium Package for Java Modernization

Designed to support:

Java version upgrades
Dependency modernization
Jakarta EE migrations
Liberty modernization
UI modernization initiatives
Testing and validation workflows

IBM Bob Premium Package for IBM i

Provides specialized tooling and workflows for:

RPG
COBOL
CL
DDS
SQL
IBM i modernization activities

IBM Bob Premium Package for IBM Z

Supports enterprise-scale modernization efforts for mission-critical mainframe environments, helping teams understand, plan, validate, and execute transformations across large application estates.

These Premium Packages create a pathway for applying agentic development not only to new software but also to the systems enterprises depend on every day.

Why This Matters

The significance of this announcement goes beyond deployment flexibility.

For many organizations, the applications most in need of modernization are also the most difficult to expose to external AI services. Security constraints often prevent AI adoption exactly where modernization assistance would provide the greatest value.

The self-hosted deployment option helps close that gap.

Instead of limiting AI to non-critical projects, enterprises can begin extending agentic workflows into:

Sensitive applications
Regulated environments
Legacy modernization initiatives
Mission-critical software systems

while maintaining appropriate control over models, infrastructure, and operational boundaries.

Final Thoughts

The conversation around AI-assisted development has evolved from "Can AI generate code?" to much larger questions:

Can AI help modernize decades-old systems?
Can AI operate within regulated environments?
Can AI fit existing enterprise architectures?
Can organizations maintain control while benefiting from innovation?

IBM Bob's self-hosted deployment option is a step toward answering those questions.

By giving organizations greater control over deployment architecture, model selection, infrastructure strategy, and modernization workflows, IBM Bob expands the reach of agentic software development into some of the most important workloads enterprises manage today.

📖 Read the full announcement:
👉 IBM Bob expands to self-hosted environments for sensitive and mission-critical enterprise software
👉 Start your IBM Bob trial

What Developers Are Saying About IBM Bob #12

Abhay Rao — Fri, 02 Oct 2026 15:33:29 +0000

AI coding assistants are often evaluated on how much code they can generate. But for experienced developers, one of the most important qualities is something harder to measure: contextual understanding.

That's why this feedback from Luis Fabricio de Llamas, Senior Java Developer and Developer Advocate at Act Digital, stood out to me.

"Bob demonstrated a level of intelligence and contextual understanding that goes beyond anything I've seen in other tools."

What I find interesting about this observation is that software development is rarely just about writing code. Most engineering work involves understanding requirements, navigating existing architectures, working within team conventions, and connecting changes across multiple systems.

The difference between a useful AI assistant and a great one often comes down to context. Can it understand the surrounding codebase? Can it follow architectural patterns? Can it reason about the intent behind a change rather than simply generating syntax?

As projects grow in complexity, contextual understanding becomes increasingly valuable. Developers spend less time creating isolated snippets and more time working within larger systems. AI tools that can retain and apply that context have the potential to become meaningful engineering partners rather than simple code generators.

Ultimately, smarter code starts with deeper understanding.

⛓️‍💥 Try IBM Bob for free: Start Free Trial

What Developers Are Saying About IBM Bob #11

Abhay Rao — Thu, 01 Oct 2026 09:06:35 +0000

One of the most interesting applications of AI isn't necessarily writing code. Sometimes the biggest impact comes from helping people learn, understand complex material, and navigate challenges more efficiently.

That's why this feedback from Shigeru Ueda, Manager at Chukyo TV Broadcasting Company, caught my attention.

"We load all the course materials into Project Bob and work together to identify where students are likely to get stuck. It supports them throughout the process, from understanding the context to polishing their writing, which reduces back and forth and saves a lot of time."

What stands out here is how IBM Bob is being used as more than a technical assistant. By analyzing learning materials and identifying areas where students may struggle, it becomes a collaborative tool that helps improve understanding before problems arise.

The workflow also extends beyond answering questions. Bob can assist learners in understanding concepts, refining ideas, improving written communication, and navigating complex subject matter with greater confidence. The result is less time spent going back and forth for clarification and more time focused on learning and progress.

As AI adoption continues to grow, some of the most valuable use cases may come from augmenting human learning, communication, and problem-solving rather than simply automating tasks.

Guided Java Modernization with IBM Bob: Moving Enterprise Java Forward with Confidence

Abhay Rao — Thu, 01 Oct 2026 06:49:45 +0000

For many organizations, Java modernization is no longer optional.

Applications that have been running successfully for years often need to be upgraded to newer Java versions, modernized for cloud-native environments, adapted to modern user experiences, and secured against emerging vulnerabilities. The challenge isn't performing a single upgrade. The challenge is coordinating these changes across large, interconnected application portfolios without introducing unnecessary risk.

This is where the IBM Bob Premium Package for Java Modernization becomes particularly interesting.

Rather than treating modernization as a collection of disconnected tasks, IBM Bob approaches it as a guided workflow that spans assessment, planning, implementation, testing, governance, and validation. The goal isn't simply generating migration code. The goal is helping teams modernize complex Java environments while maintaining quality, traceability, and operational stability.

Modernizing Java Versions at Scale

One of the most common modernization initiatives involves upgrading Java runtime versions.

Organizations still running Java 8 applications often face significant challenges when moving toward Java 11, Java 17, Java 21, or even Java 25. These upgrades frequently involve more than updating a configuration file.

Teams need to understand:

Dependency compatibility
Framework support
Configuration changes
Deprecated APIs
Build pipeline impacts
Runtime behavior changes

The IBM Bob Premium Package for Java Modernization provides guided workflows that help analyze existing environments, identify compatibility issues, coordinate required updates, and validate the results.

Whether applications are built on Spring, IBM Enterprise Build of Quarkus, or other frameworks, the objective is to reduce the effort involved in planning and executing upgrades across an application portfolio.

Modernizing Legacy User Interfaces

Java modernization isn't only about runtimes.

Many organizations continue to operate applications built with frameworks such as:

JSF
Struts
Other legacy Java UI technologies

These systems often contain years of accumulated business logic that organizations cannot simply replace.

The Premium Package supports UI modernization initiatives by helping teams transform existing interfaces into modern experiences using frameworks such as:

React
Angular
Vue

The focus is on preserving business behavior while modernizing the presentation layer, reducing the risk associated with large-scale rewrites.

Moving to Liberty and Jakarta EE

Another significant challenge for enterprise teams is the transition from older Java EE environments to Jakarta EE and modern cloud-native runtimes.

The Premium Package provides guided modernization workflows designed to help organizations move toward IBM Liberty, IBM's cloud-native Java runtime.

Using AI-assisted guidance together with deterministic modernization workflows, teams can:

Modernize application configurations
Update runtime dependencies
Validate deployments
Reduce operational complexity
Accelerate cloud-native adoption

For organizations working through large modernization programs, having structured guidance can reduce much of the uncertainty traditionally associated with migration projects.

Improving Quality Through Continuous Testing

One of the most important aspects of modernization is validation.

Modernization projects frequently fail not because the migration itself was incorrect, but because teams lack confidence that existing functionality still works afterward.

IBM Bob addresses this by incorporating Java unit testing directly into the modernization workflow.

Teams can:

Generate new unit tests
Update existing tests
Improve test coverage
Execute build-test-diagnose-fix cycles
Validate modernization changes continuously

This creates a more controlled modernization process where testing becomes part of the workflow rather than an afterthought.

Key Benefits of the Premium Package

What stands out most is that the Premium Package is designed around enterprise modernization realities rather than isolated coding tasks.

Accelerate Upgrades

By combining dependency analysis, version compatibility checks, code updates, and validation into a single workflow, teams can modernize applications more efficiently and reduce manual effort.

Reduce Migration Risk

Modernization occurs through smaller, verifiable steps supported by validation checkpoints, approvals, and traceable activities. This helps organizations maintain production stability while evolving critical systems.

Preserve Business Logic

One of the greatest risks in modernization is accidentally changing application behavior. Guided workflows help teams modernize runtimes, frameworks, and interfaces while preserving the business processes and rules that applications depend on.

Govern Every Change

Modernization projects often involve compliance, security, and audit requirements. The package incorporates:

CVE analysis
Documentation support
PR-ready outputs
Approval workflows
Governance controls

This helps ensure modernization efforts remain secure, traceable, and production-ready.

Beyond Java: A Broader Modernization Strategy

The Java Premium Package is part of a broader IBM Bob modernization strategy.

Additional premium packages are available for:

IBM Z Modernization

Helping organizations modernize complex mainframe applications through platform-specific expertise, guided workflows, and enterprise-grade modernization support.

IBM i Modernization

Helping organizations modernize IBM i applications through deep support for:

RPG
COBOL
CL
SQL
DDS
QSYS workflows

These packages recognize that modernization challenges look different across platforms and require specialized tooling and expertise.

Learn More

Explore IBM Bob Pricing
IBM Bob Premium Package for Java Modernization
Modernize IBM i Applications
Accelerate Mainframe Modernization#AgenticDevelopment #CloudNative