Six domains. Three tiers. The complete agentic technology landscape mapped to IBM i specifically — what's foundational to any production deployment, what's emerging, and where most implementations fail.
This is not a tutorial. It is not a vendor comparison. It is a map — the one that tells you what connects to what, what's foundational versus emerging versus noise, and how to sequence your attention.
Foundational — required working knowledge regardless of where your organization is in the transition. These domains underpin every agentic deployment. Emerging — direction is clear, path is still forming. Vocabulary and evaluation posture now, implementation later. Critical Gap — the domain most commonly underbuilt until a deployment fails. Treat it as infrastructure, not afterthought.
Each domain includes an IBM i-specific read: what it means for environments running RPG, DB2 for i, and a platform with object-level security and journal-based audit trails built in since 1988. Where IBM i shops have a structural advantage, this map names it. Where the gap needs closing, it names that too.
The 1% at full deployment aren't seeing incremental gains — they're seeing 3–5× productivity. McKinsey tested 25 variables across 2,000 organizations. The single highest-ranked factor was workflow redesign, not tool selection. The chasm is not a technology problem. It is a readiness problem. The organizations that cross it first compound their advantage permanently.
Every domain in this map intersects with IBM i specifically. This is the layer underneath — what each domain means for the practitioner who has built their career on the platform.
Watsonx and Granite give IBM i shops a path that stays inside the IBM relationship, which matters for shops where procurement, security review, and vendor management all live inside that relationship already. Claude and ChatGPT are what your developers are actually using, whether or not they've told you. Shadow AI is already in your environment. The real question isn't which model to standardize on — it's whether you've made the choice deliberately or by default.
That distinction matters when the codebase is RPG, not Python. The practitioner who learns to work with Bob now — understanding where it helps, where it misreads the platform, where human judgment still governs — has an advantage that compounds. The shop that waits discovers the gap when it's larger. Steve Will has been direct: the platform moves to a fully agentic development model within the next few years. The question is whether that happens to you or with you.
Before you evaluate any agentic vendor, ask: what protocol does your agent use to connect to IBM i? If they don't know what MCP is, the conversation ends there. The IBM i MCP server is a published, IBM-maintained repository — the access layer is being built. LangGraph orchestration is what makes multi-step agent workflows coherent rather than brittle. Most IBM i shops won't build these layers — but you need the vocabulary to evaluate who is building them on your behalf.
PowerVS keeps the IBM i workload inside IBM's infrastructure while opening the agentic surface. Where does your business logic live, who controls it, and what's the access model when agents operate against it at scale? That's the infrastructure question the agentic economy forces. The capex reckoning is real — Morgan Stanley revised 2026 hyperscaler investment from $650B to $805B in four weeks. The shops that transferred hardware risk to IBM are better positioned than the ones still deciding.
Every piece of business logic embedded in your RPG programs, every Db2 table encoding decades of operational decisions — the agent that can access that intelligence is more capable than one that can't. Mapepire is part of the access layer. Natural language interfaces to Db2 are part of it. Understanding the access architecture your agents need before you build it is work only you can do — because only you know what the data actually means.
Object-level security, journal-based audit trails, profile management — the governance primitives are there. What the agentic layer requires is extending that logic to cover agent identity and agent actions specifically. Who is the agent acting as? What can it access? Where does the audit trail live when an agent makes a decision that affects a customer? The IBM i practitioner who already understands audit trails and access controls has a shorter path to agent-ready governance than a cloud-native shop building those muscles for the first time.