From Demos to Deployment: Building Agentic AI Systems That Work in the Enterprise

Executive Summary: Why This Matters Now

Agentic AI:  autonomous, goal-seeking systems powered by large language models (LLMs) is rapidly transitioning from novelty to necessity. While developers are building impressive prototypes, many large enterprises remain stuck in pilot purgatory: mesmerized by potential, but unable to translate demos into durable systems that generate value at scale.

Two leading visions frame the conversation:

• Andrej Karpathy’s Software 3.0 introduces LLMs as a new programmable platform. He urges builders to think in terms of prompt-driven computing and partial autonomy, where humans and machines collaborate in tight, feedback-rich loops.
• McKinsey, in contrast, paints a top-down picture of “agentic meshes”: interconnected agents embedded in business processes, orchestrated across workflows, and governed through executive-led change management.

Each view highlights an essential dimension of the challenge but neither is complete on its own. For BFSI leaders navigating complexity, compliance, and legacy systems, the answer isn’t to choose between the two. It’s to synthesize the best of both: engineering grounded in developer reality, executed within enterprise-aware constraints.

This article offers a practical blueprint for that middle path.

1. The Two Worlds of Agentic AI

Karpathy and McKinsey speak to different audiences and that’s their strength. One comes from the codebase. The other, from the boardroom.

Karpathy's view is that we’ve entered the Software 3.0 era, where code is no longer the sole input to machines. Prompts, context windows, and tool-use turn LLMs into a new class of programmable entities. His vision celebrates fast iteration, developer control, and human-in-the-loop systems, think co-pilots, not commanders.

McKinsey’s framing is enterprise-first. Their “agentic mesh” is a roadmap for C-suites: build distributed agents across value chains, layer them into digital workflows, and manage the transformation through governance, OKRs, and talent enablement.

The tension between these views is real but productive. The future belongs to organizations that can move like builders, but scale like enterprises.

2. The Shift: Why Agentic Systems Are Different

Agentic AI isn’t just “better chatbots” or “smarter RPA.” It’s a shift in the computing model. Unlike traditional software, agents:

• Plan: They decompose goals into subtasks dynamically.
• Use tools: They invoke APIs, query databases, or write code.
• Retain short-term memory: Context windows give them ephemeral working memory, though long-term state is still an unsolved challenge.
• Engage humans: They often require supervision, prompts, and confirmation especially in regulated environments like BFSI.

In this model, a "program" isn't always deterministic or hand-coded. It might be a prompt, a memory state, or a tool invocation chain and this makes design, testing, and deployment much more fluid.

This is not more automation. It’s different automation.

3. Designing for Reality: What Works in Practice

Let’s ground this in what’s actually working today not what might work in a year.

3.1. Micro-Agents, Not Mega-Meshes

Rather than sprawling “agentic meshes,” we’re seeing success with narrow, single-purpose agents that handle:

• Document classification and summarization (e.g., KYC docs, contracts)
• Email triage and reply generation
• Test data synthesis
• Report preparation (e.g., compliance narratives, board packs)

These agents typically:

• Operate within a sandboxed environment
• Rely on structured prompts or few-shot templates
• Include human checkpoints for review or override

3.2. BFSI Use Case Sketches

• Summarizes audit findings from multiple reports
• Aligns them to regulatory frameworks (e.g., SOX, OSFI)
• Presents suggested responses, but final sign-off remains human

• Reads test cases or user stories
• Generates masked synthetic data
• Includes explainable logic trails for each data point created

• Parses regulatory updates
• Extracts relevant changes and impacts
• Pushes to appropriate teams with context (e.g., which products, which controls)

4. Enterprise Constraints You Can’t Ignore

Here’s where McKinsey’s structure is essential. In BFSI, no agent will survive without addressing:

4.1. Trust & Explainability

• Can a human understand why the agent acted a certain way?
• Are outputs traceable to sources?
• Do you log, score, and monitor hallucination risk?

4.2. Governance & Risk

• What change controls apply when a prompt changes?
• Who approves external tool use or API access?
• Can this agent pass a model risk validation framework?

4.3. Data Privacy & Security

• Is PII masked or excluded from LLM inputs?
• How are prompts, outputs, and logs retained and reviewed?
• Do external model calls stay within permitted jurisdictions?

The point: Agents are not immune from the rules of enterprise IT. They must adapt not bypass them.

5. Agent Engineering Stack: What You Need to Build

Building agentic systems requires new thinking across multiple layers. Here’s a simplified stack:

Think of it as DevOps meets prompt engineering. You’ll need new roles (prompt engineers, LLM architects), but also strong collaboration with infosec, compliance, and product teams.

6. Adoption Strategy: Crawl, Walk, Run

Here's a pragmatic rollout model for agentic AI in BFSI:

6.1. Crawl

• Build internal-only copilots with no external tool use.
• Instrument heavily for observability.
• Prioritize internal documentation and workflow support.

6.2. Walk

• Introduce tool-using agents for data transformation, test data, summarization.
• Establish agent development guidelines + internal review boards.
• Integrate with enterprise authentication, data masking, logging.

6.3. Run

• Embed agents into business-facing products or operations.
• Implement change control for prompts + memory state.
• Define enterprise-wide autonomy levels and risk flags.

7. Closing: What It Means to Lead the Shift

Agentic AI will not arrive as a complete solution. It will be engineered into existence piece by piece - by teams who understand both the transformative power of this new computing model and the institutional structures that must support it.

Karpathy teaches us to move fast, design with users, and build for feedback.  McKinsey reminds us to govern thoughtfully, align to goals, and scale responsibly.

Your job as a technical leader is to blend these two modes to create systems that are not just smart, but secure, sustainable, and shippable.

The agentic future isn’t about big bang deployments or flashy demos.

It’s about building intelligent systems, one verified output at a time.