Written by Yashwanth Hemaraj, General Partner at BGV
The age of AI agents has arrived—but in the enterprise world, it’s not just about flashy demos or experimental chatbots, it’s about building resilient, scalable, and trustworthy systems that can operate safely and effectively in high-stakes environments. Whether automating workflows, enhancing customer service, or powering decision intelligence, AI agents must meet a far higher bar than their consumer-grade counterparts.
Gartner projects that by 2028, a third of enterprise software will incorporate agentic AI, with 15% of day-to-day decisions made autonomously. This shift is not just about efficiency—it’s about fundamentally enhancing the quality, speed, and reliability of enterprise decision-making.
“Agentic AI will introduce a goal-driven digital workforce that autonomously makes plans and takes actions—an extension of the workforce that doesn’t need vacations or other benefits.” — Gartner
While the AI Agent market is projected to grow from a $5.1 billion market in 2024 to $47.1 billion by 2030, we see nearly half of AI proof-of-concepts being scrapped before reaching production. This surge in project failures highlights the urgent need for organizations to reassess their AI strategies and take a systematic approach towards and Agentic approach while addressing persistent challenges such as high costs, data privacy, and security risks.
For business leaders and technologists, understanding how to design, implement, and scale AI agents is no longer optional—it’s a strategic imperative. This guide synthesizes insights from frameworks from our portfolio companies such as Lyzr, Daxa, Pipeshift, Enkrypt etc. to provide a roadmap for building agents that deliver measurable business value while operating safely at scale.
In this guide, we will cover:
- What AI Agents are and why they matter for enterprises
- Core architectural elements for enterprise deployment
- Implementation patterns and orchestration models
- Safety, governance, and trust frameworks
- Future trends and strategic recommendations
So, how do you go from prototype to production? From chatbot to enterprise-ready agent? Here’s the blueprint.
What AI Agents are and Why they matter for Enterprises
Defining the AI Agent
Building an AI agent for enterprise use is fundamentally different from creating a chatbot for casual conversation.
Take customer support as an example. Chatbots have been used in customer support for many years and often times have pre-defined responses to the most basic questions that a customer may ask. In the scenario that the conversation goes beyond the scope of what it knows, it will likely offer to connect the customer to a support specialist who can handle the rest of the interaction.
On the contrary, an AI Agent could be provided the same training materials that were given to the customer support agent and also given access to all the tools a support agent would use. The customer would then enter their problem into the chat, and the AI Agent would work on the backend to get their issue by determining the right tools for the problem, figuring out the right path of execution, and overcoming issues it faces along the way. This system is much more autonomous than traditional solutions and requires significantly less oversight from humans.
An AI agent is an autonomous system that combines large language models (LLMs), tool integration, and explicit instructions to execute workflows with human-like adaptability. Unlike traditional automation that follows rigid rules, agents dynamically select tools based on context, correct course when errors occur, and operate within defined safety parameters.
Key Differentiators from Traditional Systems:
- Non-linear execution: Agents navigate complex decision trees rather than predefined paths.
- Contextual tool selection: They choose appropriate APIs/databases based on real-time needs.
- Self-correction: Advanced agents iteratively improve outputs using reinforcement learning.
For example, Microsoft’s implementation for financial reconciliation demonstrates this capability, where agents handle sensitive tasks like invoice approvals while escalating edge cases to human reviewers. Similarly, retail banking agents or insurance agents can interpret large volumes of unstructured documents, cross-reference various databases, and finalize terms while maintaining regulatory compliance.
Enterprise-grade agents are designed to:
- Operate autonomously over extended periods
- Execute sophisticated, non-linear workflows
- Handle sensitive data with rigorous security and compliance controls
- Integrate deeply with legacy systems, APIs, and modern cloud architectures
- Continuously learn and self-correct with minimal human intervention
The emergence of such agentic systems will not only justify the scale of current infrastructure investments but will unlock new levels of productivity and economic growth across industries. Success hinges on moving beyond simple automation to deliver true business transformation.
Core Architectural Components for Enterprise Deployment
Every production-grade agent requires three foundational elements:
1. Model Selection Strategy
- Accuracy-first approach: Start with largest viable LLM (e.g., GPT-4 Turbo) to establish performance baselines
- Optimization phase: Replace with smaller models (e.g., Claude Haiku) where latency/cost outweigh accuracy needs
- Evaluation framework: Implement metrics tracking precision (correct outputs), recall (completeness), and business KPIs
2. Tool Ecosystem Design
| Tool Type | Enterprise Use Case | Implementation Example |
| Data Tools | Context retrieval | CRM API queries |
| Action Tools | System interactions | Payment gateways |
| Orchestration | Workflow management | Lyzr Studio, LangChain |
3. Instruction Engineering
Best practices from building practical agents from ground up include:
- Break down workflows into individual business tasks and rules associated with those tasks
- Convert rules into LLM-friendly routines with clear success criteria for success
- Chunk complex processes into atomic steps (e.g., “Validate IBAN” vs “Process payment”) and process maps
- Identify edge cases that can potentially derail implementations and have to recover from those edge cases.
Implementation Patterns and Orchestration models
Single vs. Multi-Agent Architectures
Single-agent systems are best suited for straightforward workflows that involve fewer than 20 decision nodes and operate within limited tool ecosystems, typically with fewer than 10-12 integrated APIs. In contrast, multi-agent systems become necessary when workflows exceed 30+ conditional branches or when tool complexity leads to “API overload” measured in terms of integrated endpoints.
There are two primary implementation models for agentic systems. The first is the Manager-Agent Hierarchy, where a central controller delegates tasks to specialist agents with well defined routing processes. The second model is Decentralized Swarms, in which peer agents collaborate via shared memory to optimize workflows, as seen in supply chain optimization scenarios
Emerging Role of Agent-to-Agent Protocols in Building Modern Agentic Systems
Agent-to-agent protocols like Google’s A2A (Agent-to-Agent) and Anthropic’s MCP (Model Context Protocol) are emerging as critical infrastructure for the next generation of AI systems. These standardized communication frameworks address fundamental challenges in building sophisticated, collaborative agentic systems by enabling seamless interaction between AI agents and their tools. These protocols serve distinct but complementary roles in the agentic ecosystem:
Agent-to-Agent (A2A) Protocol
A2A is designed specifically to standardize how autonomous AI agents communicate with one another. It enables agents to discover each other, exchange information securely, and coordinate actions across services and enterprise systems. The protocol works by having agents expose a public “Agent Card” via HTTP that contains:
- Hosting/DNS information about where the agent is accessible
- Version information
- A structured list of the agent’s skills and capabilities[1]
A2A supports multiple communication methods based on task requirements:
- Request/Response with Polling (standard HTTP)
- Server-Sent Events (SSE) for short-running tasks
- Push Notifications for long-running tasks
Model Context Protocol (MCP)
Unlike A2A’s focus on inter-agent communication, MCP standardizes how AI agents connect to and utilize external tools and services. It provides a universal interface for tool integration that has been likened to a “USB-C port for AI applications.”
MCP consists of three primary components:
- MCP Hosts: The AI agents seeking access to tools or data
- MCP Clients: Protocol intermediaries facilitating communication
- MCP Servers: Programs that expose specific capabilities through the protocol[3]
Complementary Roles in Agentic Systems
Rather than competing alternatives, these protocols operate at different levels of the AI architecture:
| Category | A2A (Agent-to-Agent) | MCP (Model Context Protocol) |
| Primary Goal | Enable inter-agent task exchange | Enable LLMs to access external tools or context |
| Designed For | Communication between autonomous agents | Enhancing single-agent capabilities during inference |
| Focus | Multi-agent workflows, coordination, delegation | Dynamic tool usage, context augmentation |
| Execution model | Agents send/receive tasks and artifacts | LLM selects and executes tools inline during reasoning |
| Security | OAuth 2.0, API keys, declarative scopes | Handled at application integration layer |
In a typical enterprise workflow:
- A user submits a complex request to an AI system
- The orchestrating agent uses A2A to delegate subtasks to specialized agents (analytics, HR, finance)
- Those specialized agents use MCP internally to invoke search functions, fetch documents, or compute results
- Results are returned as artifacts via A2A, enabling end-to-end collaboration
This architecture effectively separates inter-agent communication (A2A) from intra-agent capability invocation (MCP), making systems easier to compose, scale, and secure. As these protocols mature, they will likely become as fundamental to agentic AI as HTTP became to the web—invisible infrastructure that enables a thriving ecosystem of interoperable, collaborative intelligent systems.
Ensuring Safety and Reliability in Agent Systems
To ensure safety and reliability in agentic systems, a layered guardrails framework is essential. Real-time decision frameworks need to provide agent decisions while maintaining minimum latency overhead. Human-in-the-loop strategies further enhance system robustness. However, it may cause latency and break in user flow. Design must consider these aspects of user experience and only use human in the loop for high value and high risk triggers.
| Layer | Techniques | Enterprise Example |
| Data Safety | PII masking, RBAC controls | Healthcare record processing |
| Content Safety | Toxic language detection, fact-checking | Content moderation |
| Process Safety | Confidence thresholds, circuit breakers | High-value Fraud Detection |
A well designed closed loop system has the measurement mechanisms and self-optimization built in. Machine learning plays a pivotal role in enhancing the effectiveness of AI agents by enabling them to learn, adapt, and improve over time. Through techniques like supervised, unsupervised, and reinforcement learning, AI agents can analyze data, make informed decisions, and refine their actions based on outcomes. This learning capability allows agents to perceive their environment, understand complex data, and execute tasks autonomously, leading to increased efficiency, accuracy, and adaptability in dynamic environments. As a result, AI agents become more reliable and efficient in performing tasks and making decisions.
Future Trends and Strategic Recommendations
As enterprises navigate the next wave of AI adoption, the emergence of advanced agentic systems will be at the center of transformational change. These systems won’t just automate—they will redefine how work gets done by operating autonomously over extended periods and executing sophisticated workflows with minimal human intervention.
The future belongs to enterprises that proactively position themselves for this shift. Success will require not just technical implementation, but a comprehensive strategy that addresses talent, infrastructure, and governance.
Key Strategic Recommendations for Business Leaders
- Talent Development:
Upskill teams in prompt engineering, agent monitoring, and AI system governance. The rise of agentic AI requires new skillsets that combine domain expertise with deep technical understanding. Consider building dedicated AI governance and agent operations teams to manage these complex systems effectively.
- Infrastructure Modernization:
Build API-first ecosystems to enable seamless tool integration and future scalability. Enterprises must move beyond legacy systems and adopt cloud-native architectures that support modular agent frameworks and real-time decision-making.
- Governance Frameworks:
Implement robust AI TRiSM (Trust, Risk, and Security Management) protocols to manage model fairness, transparency, and accountability. As AI agents take on more critical decision-making roles, governance will no longer be optional—it will be a board-level imperative.
- Adopt a Phased Implementation Roadmap:
Enterprises should take a systematic, phased approach to AI agent deployment:
- Phase 1 (0–3 months): Pilot single-agent workflows with limited tools but strong guardrails in place.
- Phase 2 (3–6 months): Expand to multi-agent systems for more complex, cross-functional processes such as order-to-cash.
- Phase 3 (6–12 months): Scale to enterprise-wide deployment with centralized governance and oversight.
This strategic roadmap enables organizations to capture early wins, build institutional knowledge, and progressively expand AI capabilities with confidence.
Conclusion: The Agent-Driven Enterprise
The organizations leading the AI revolution aren’t just adopting agents—they’re redesigning their operations around autonomous systems. The path forward requires strategic investment across three key areas: talent development, infrastructure modernization, and governance frameworks.
As NVIDIA’s recent Agent Blueprints initiative demonstrates, the future belongs to businesses that embed agentic capabilities into their DNA. The world is moving faster than ever, and within the next 18 months, new market leaders will emerge—those who have successfully scaled agentic systems into the fabric of their business processes.
For forward-thinking leaders, the time to act is now—before competitors lock in the productivity and efficiency gains of this multi-billion-dollar revolution.
