From AI Agents to Agentic AI: A Roadmap for Regulated Enterprises

Why siloed AI agents stall - and what agentic AI changes

Many banks, insurers, and healthcare organizations have already dabbled in AI agents. You might have a support agent that resets passwords, a chatbot that answers policy questions, or a bot that helps reconcile transactions. These task-specific agents often deliver quick wins - but when enterprises try to scale them, they hit familiar walls: fragmented experiences, governance blind spots, and brittle automations that do not survive real-world complexity. The result is a "widget farm" of uncoordinated agents rather than a strategic shift in how work gets done.

The emerging answer is not to abandon agents, but to evolve toward agentic AI: systems that can coordinate multiple agents around shared goals, adapt to changing context, and operate under explicit governance. Salesforce describes the "agentic enterprise" as one where humans and AI agents work as a unified digital workforce, with AI handling high-volume workflows and humans focusing on high-judgment tasks (Salesforce: What is the agentic enterprise?). IBM’s overview of agentic AI makes the same point: the key shift is from individual models responding to prompts to orchestrated agents pursuing goals autonomously while remaining observable and controllable (IBM: What is Agentic AI?).

The challenge for regulated enterprises is that you cannot simply flip a switch from "agents" to "agentic". You need a roadmap that respects your risk appetite, regulatory obligations, and existing process architecture. You also need to recognize that un-orchestrated agents can exacerbate operational and compliance risk. Moveworks notes that siloed agents often generate notification overload, inconsistent answers, and governance challenges when their actions are not centrally coordinated (Moveworks: AI agent orchestration).

Gigster warns that enterprises rushing into agentic AI without preparing data, integrations, and governance risk "sorcerer’s apprentice" situations where autonomous systems behave unpredictably (Gigster: What is agentic AI and how to prepare). For BP3’s clients, especially in financial services and insurance, the path forward is to treat agentic AI as an evolution of your intelligent automation program, not a replacement. It starts with acknowledging that today’s AI agents are often embedded at the edge - in SaaS tools, support platforms, or custom apps - with little visibility from central architecture, risk, or compliance teams.

The goal of the roadmap is to pull those capabilities into a governed orchestration layer, gradually increase their autonomy under clear guardrails, and connect them to your core processes in a way that reduces, rather than amplifies, risk.

A staged roadmap from task agents to governed agentic workflows

Designing a roadmap from isolated AI agents to agentic AI begins with an honest inventory of where you are today. In most enterprises we work with, there are three broad stages of maturity that often coexist across teams.

In Stage 1, you have single-task agents embedded in specific systems: a chatbot in your contact center, an assistant in your CRM, or an expense-processing bot inside your finance platform. These agents are useful, but they lack shared context and governance. The first roadmap step is to surface them to a central view: document what each agent does, what systems it touches, what data it can access, and what controls exist around it. This aligns with the advice from Gigster and Oteemo: before you scale agentic AI, ensure your data, integrations, and AI culture are ready, and avoid scattering autonomy across opaque tools (Gigster: What is agentic AI and how to prepare; Oteemo: What is Agentic AI?).

In Stage 2, you introduce a unified orchestration layer and make it the default way agents participate in business processes. Rather than having each SaaS tool initiate its own mini-workflow, you bring requests through a "front door" - for example, a portal or chat entry point - and let a router agent classify and route them into orchestrated workflows. Vendors like Tonkean and Moveworks demonstrate this pattern in practice: they use a central agent to triage and dispatch work to specialized AI agents and backend systems, maintaining full context and audit trails (Tonkean: Agentic orchestration platform; Moveworks: Agentic AI in the enterprise). During this stage, you also begin to encode autonomy levels into your orchestration: which tasks can agents perform end-to-end, which must escalate to humans, and which are off-limits. In banking, that might mean allowing agents to fully resolve low-risk IT tickets or HR queries, while requiring human approval for any changes to customer terms, pricing, or risk settings. You start to define and test agents as composable components in BPMN processes or low-code flows, rather than ad-hoc scripts.

Stage 3 is where you earn the "agentic" label: planners and supervisor agents can now coordinate multiple specialized agents to execute end-to-end workflows across systems, within policies you have codified. Towards AI’s banking orchestration article describes patterns like sequential handoffs, parallel fan-out, supervisor–worker models, and event-driven orchestration for complex cases such as mortgage underwriting or fraud response (Towards AI: Workflow Orchestration for Agentic AI). In this stage, your orchestrator becomes the control plane: it maintains state, enforces rules, manages exceptions, and logs every step for audit.

Throughout these stages, BP3 recommends starting with a few carefully chosen journeys: KYC onboarding, claims handling, or dispute resolution are typical choices. We then introduce BP3 accelerators like our Agentic AI Compliance Monitor where they make sense - using agents to watch contracts, logs, and workflows for emerging risks, and orchestrating human review when thresholds are crossed (BP3 Agentic AI Compliance Monitor).

The roadmap is not a linear march; different lines of business may sit at different stages. The key is that they share the same patterns, contracts, and guardrails.

Measuring value, risk, and readiness along the way

No roadmap is complete without a way to measure progress and manage risk. Moving from scattered AI agents to agentic AI introduces new dependencies and expectations, so you need dashboards and metrics that reflect both value and control. On the value side, orchestrated agentic systems should be able to demonstrate improvements that go beyond "number of tickets handled by AI".

Towards AI suggests measuring workflow completion rate (how many journeys finish end-to-end without manual rescue), handoff success (how often agent-to-agent or agent-to-human transitions succeed without rework), and time-to-decision reduction. Moveworks reports that orchestrated agents can cut processing times for common workflows by 20–80%, especially in IT support, onboarding, and expense management (Moveworks: AI agent orchestration).

These are the types of metrics that resonate with COOs and CFOs as evidence that agentic AI is more than a lab experiment. On the risk and readiness side, you should track the percentage of workflows with explicit autonomy ladders, the coverage of guardrails (how many workflows have input, output, and execution guards defined), the proportion of agent actions with full audit trails, and the number of incidents attributable to agent behavior.

Frameworks such as the NIST AI Risk Management Framework and IBM’s governance guidance for agentic AI provide a vocabulary for this: they emphasize clear decision rights, model lifecycle controls, and continuous monitoring as prerequisites for scaling (IBM: What is Agentic AI?). Practically, this means investing in three capabilities as you move along the roadmap:

• A central catalog of agents and workflows, with owners, scopes, and risk classifications.
• A shared observability stack where architecture, risk, and operations teams can see what agents did, where they succeeded, and where they failed.
• An enablement program that trains designers, developers, and process owners on how to work with agentic patterns - BPMN with agent steps, DMN with agent-assisted decisions, and human-in-the-loop designs.

For BP3, this is where our heritage in process modeling and enablement pays off. We help clients express their critical journeys in BPMN, surface opportunities for agents, and then layer in agentic capabilities over time. We treat agentic AI not as a monolith, but as a set of repeatable patterns that can be rolled out, measured, and iterated safely.

Ultimately, the roadmap from AI agents to agentic AI is as much an organizational journey as a technical one. It requires IT, risk, operations, and business stakeholders to align on what "good" looks like: where autonomy is acceptable, what evidence is required, and how to respond when agents fall short.

Enterprises that embrace this as a deliberate transformation - rather than a patchwork of disconnected pilots - will be the ones that turn agentic AI from another buzzword into a durable competitive advantage in regulated markets.