Amit Tiwari
SVP of Agentic AI, The Silicon Partners · 5× UiPath MVP
10 min read · Agentic Automation · Enterprise Architecture
Most conversations about Agentic AI start in the wrong place.
They start with the technology — large language models, retrieval-augmented generation, agent frameworks, multi-agent protocols. Executives sit through demonstrations of impressive capabilities and walk away with one of two reactions: either they’re excited about something they can’t yet operationalize, or they’re skeptical because they’ve heard the same “transformational” pitch about every technology wave for the past decade.
I’ve been in both of those rooms. And after leading agentic AI programs across dozens of enterprise environments, I can tell you: the technology is the easy part.
The hard part — and the part that actually determines whether your organization becomes an autonomous enterprise or stays a collection of automated tasks running in silos — is architecture.
For the past several years, enterprises have invested heavily in automation. RPA programs scaled. Bot libraries grew. Process owners identified hundreds of automation candidates, and delivery teams built them. By most measures, it worked.
But here’s what we got wrong: we automated tasks, not outcomes.
A bot that extracts invoice data runs in one system. A bot that validates purchase orders runs in another. A workflow that routes exceptions to a human queue is in a third. None of them talk to each other. None of them adapt when business rules change. None of them know that the invoice, the PO, and the exception are all part of the same business event.
We built efficiency in silos. And siloed efficiency has a ceiling.
The enterprises pulling ahead aren’t the ones with the most bots. They’re the ones that asked a different question: not “which tasks can we automate?” but “which outcomes do we want to make autonomous?”
— Amit Tiwari, SVP of Agentic AI
That’s the shift from automated to autonomous. And it changes everything about how you architect your programs.
Before we go further, let me be precise about something, because the market has made “agent” mean everything and therefore nothing. There are three distinct levels of maturity — and confusing them is the most common strategic mistake I see.
01
Traditional automation enhanced by AI insights — ML models, NLP, AI OCR — still following predefined paths. Smarter, but not autonomous.
RPA bot using AI OCR to extract invoice data but following a static, predefined workflow
02
Software entities that perceive environments, set intermediate goals, take action, observe results, and adapt. They pursue objectives, not scripts.
Agent managing invoice approvals by evaluating evolving business rules and learning from resolution patterns
03
Agents managing entire processes across systems, departments, and decision points. Humans involved only at genuine exception points.
Agent managing end-to-end vendor onboarding across procurement, legal, and finance
The distinction matters because the architecture for each is fundamentally different. You cannot retrofit an agentic program onto an RPA foundation without rethinking how work is organized, governed, and measured.
Here’s what I’ve learned from building these systems: an agent that can answer questions and generate responses is a capable agent. It is not an enterprise-ready agent. Those are not the same thing.
Enterprise-ready means the agent operates within a three-layer architecture. Each layer is essential. None is sufficient alone.
The mistake I see most often in the workforce model is organizations trying to use AI agents for everything — including tasks that robots do faster, cheaper, and more reliably. The second mistake is keeping humans in loops they don’t need to be in. Both degrade the system’s value.
On orchestration: this is the layer that most enterprise teams underestimate, and it’s where UiPath Maestro has become a significant enabler. Without orchestration, you don’t have an agentic program. You have a set of capable agents running independently with no coordination layer. That’s not autonomous enterprise architecture. That’s chaos with better technology.
Governance is not a constraint on agentic AI. It’s the enabler. The enterprises I’ve seen scale fastest are the ones that built their governance layer first.
— Amit Tiwari
I’m skeptical of any implementation approach that promises enterprise-wide transformation in a single deployment. The most successful agentic programs follow a phased progression that expands autonomous action incrementally — while building the organizational muscle and the trust to support it.
Phase 1
Automate document extraction and structuring. When the system encounters a genuine exception, route it to a human with full context. Humans are still deciding and acting, but the coordination burden is lifted.
Phase 2
Agents evaluate, reason, and draft a recommended action. Humans shift from executing work to reviewing and approving recommendations. A profound change — from processor to judge.
Phase 3
Agents act end-to-end — evaluate, decide, communicate, and continue — generating a full audit packet at every step. Humans notified of completions, flagged only on confidence threshold exceptions.
Phase 3 does not mean humans are removed from the enterprise. It means humans are deployed where they create irreplaceable value — in judgment calls that require context, empathy, accountability, or creativity. Everything else runs.
Let me make this concrete with a use case we’ve deployed in production.
A global enterprise runs its vendor onboarding process across three functional areas — procurement, legal, and finance. Before agentic orchestration, the process took 14 days on average, touched seven systems, and required 23 manual handoffs. Exceptions added additional days and required senior staff intervention.
We rebuilt this as a three-phase agentic program. In Phase 1, agents extracted and structured all vendor documentation, bots validated against SOPs, and exceptions were routed to procurement with full context. In Phase 2, agents evaluated vendor risk profiles and drafted approval recommendations. Procurement managers reviewed rather than investigated. In Phase 3, straight-through processing for standard vendors — agents evaluate, bots execute the ERP record creation, humans receive a completion notification and audit packet.
Vendor onboarding cycle time for standard vendors
Manual handoffs eliminated across the process
Human involvement focused on exceptions and final approvals
For enterprises running SAP — which describes most of the Global 2000 — agentic automation has a specific and significant opportunity. SAP is the system of record for the most consequential business processes in the enterprise: financial close, procurement, supply chain, HR, and customer operations. It is also a system where manual effort, exception handling, and cross-system coordination remain stubbornly high.
At TSP, we’ve spent fifteen years building deep expertise at the intersection of SAP and intelligent automation. When we bring agentic AI into that equation, the results are material.
Agents retrieve GL codes by context and policy. Robots execute posting in SAP. Exceptions auto-routed with full supporting data.
>98% posting accuracy
Agents read and interpret customer quotation emails. Robots extract, structure, and post orders directly into SAP.
Significant cycle time reduction
End-to-end document extraction, SOP validation, and ERP record creation. Fully autonomous for standard vendors.
14 days → under 48 hours
Days of manual investigation collapsed to minutes. Agents validate, bots update records, agents draft customer responses.
300+ hours saved per month
The opportunity in SAP is not about replacing the platform. It’s about removing the manual work that accumulates around the platform — the coordination, the exception handling, the cross-system data movement that no one designed as a human task but that humans ended up doing anyway.
I’ve now had a version of this conversation with hundreds of enterprise technology leaders. The ones whose agentic programs are delivering results share three characteristics that the ones still in pilot phase do not.
01
They didn’t ask “where can we deploy agents?” They asked “which business outcomes are we not achieving because of coordination complexity?” Then worked backward to architecture.
02
Counterintuitive, but the organizations scaling fastest designed their observability, audit, and escalation frameworks first. That gave them the confidence to expand autonomous action.
03
Agentic automation changes how teams are organized, what skills matter, how performance is measured. The organizations that addressed this explicitly are where adoption is accelerating, not stalling.
The autonomous enterprise is not a vision statement. It is not a technology investment. It is an architecture decision that you make or defer every quarter.
The question I ask every technology leader I work with is not “are you interested in agentic AI?” Almost everyone is. The question is: which outcome do you want to make autonomous in the next ninety days?
If you have an answer to that question — even a rough one — you have the starting point for a program that compounds. The first autonomous process builds the governance muscle. The second extends the orchestration layer. The third reaches across systems. By the time you’re asking about enterprise-wide transformation, you’ve already done it.
If you don’t have an answer yet, that’s what the conversation is for.
Amit Tiwari
SVP of Agentic AI, The Silicon Partners · 5× UiPath MVP
Amit leads TSP’s Agentic AI practice, working with global enterprises on the architecture and implementation of autonomous enterprise programs. TSP is a UiPath Diamond Partner and SAP Gold Partner with 800+ professionals and 250+ enterprise transformations delivered. Connect with Amit on LinkedIn or visit thesiliconpartners.com.
Schedule a complimentary 30-minute working session with TSP’s Agentic AI practice. No slides-only pitch — just an honest conversation about architecture and where to start.
