In my years building DevRev, we’ve seen AI evolve from an exciting capability into the foundation of how enterprises think about teamwork and customer experiences.
But too often, leaders assume building enterprise-grade AI agents is just another software project. In reality, it’s closer to running your own AI service company inside your company.
That’s why so many organizations stumble when moving from a demo to production. The data reflects this reality: MIT’s 2025 NANDA report found that 95% of generative AI pilots deliver no measurable P&L return, with just 5% extracting real value.
Why the chasm? It isn’t about model quality or regulation. It’s the approach. This is because building a reliable, enterprise-ready AI platform is a different game altogether, one that requires infrastructure, governance, security, and discipline that go far beyond a prototype.
Let’s break that down.
The mirage of the first demo
Every enterprise AI journey begins the same way: an LLM connected to a vector database using RAG (retrieval-augmented generation). It looks great in a demo with contextual answers, quick responses, even some integrations.
But moving from “it worked in POC” to “it works reliably, for everyone, in production” introduces an entirely different class of complexity:
- How do you update the model without breaking accuracy?
- How do you guarantee answers respect entitlements and privacy rules?
- How do you ensure the agent doesn’t hallucinate under pressure?
- How do you handle failures when dozens of agents are interacting simultaneously?
A proof of concept can be built in weeks. But if you choose to build, you’re not just coding an agent, you’re building an AI-native operating system for your enterprise.
What it really takes
To transform a POC into a production-grade AI platform that’s resilient, compliant, and scalable, you’ll need:
1. Core infrastructure & middleware
As Forrester notes in its 2025 AI predictions, enterprises seeking to build agentic AI architectures face significant complexity, requiring multiple models, sophisticated RAG stacks, advanced data architectures, and specialized expertise.
At its core, building enterprise AI agents means assembling and maintaining a full stack of infrastructure and middleware:
- Data plumbing at scale. Integrating enterprise systems isn’t just about stringing APIs together. It involves:
- Operational systems (Tools and MCPs) with bidirectional write-backs.
- Knowledge systems (vector databases, indexing pipelines) to make data searchable and contextual.
- Analytics layers to pull insights from structured and unstructured data.
- Inference serving so models can actually understand, process, and deliver the right information in real time.
- Authorization and permissions. Implementing role-based access so sensitive data never leak
- Orchestration layers. Enterprises rarely rely on a single LLM. You need multi-model orchestration for redundancy, cost control, and performance.
- Scaffolding for improvement. Systems for model versioning, evaluation, and feedback loops to ensure agent behavior improves over time rather than deteriorates.
Setting up the infrastructure required internally is rarely the best use of your resources, especially when they can focus on solving real business problems.
Forrester highlights this point, noting that “Mature companies will recognize these limitations and opt to collaborate with AI service providers and systems integrators, leveraging their expertise to build cutting-edge agentic solutions.”
2. Data governance
If infrastructure is the skeleton of enterprise AI, governance is its immune system. Since AI models can be complex “black boxes,” governance is essential for understanding and managing their behavior. You’ll need:
- Data quality & bias mitigation. Poor data quality is still the number one reason AI projects collapse. Enterprises need validation, cleansing, and continuous monitoring for bias in both training and operational datasets. Accuracy, representativeness, and fairness are non-negotiable.
- Data leakage prevention. Encrypt data both at rest and in transit. Implement robust security measures to prevent prompt injection and other attacks that could cause your model to leak sensitive training data.
- Privacy & security. This involves using anonymization, tokenization, or differential privacy to protect personal information embedded within training data, while honoring explicit user consent and complying with regional regulations.
- Transparency & accountability. You need to track lineage (where data came from and how it was transformed), monitor model behavior, and maintain auditability to keep AI accountable and compliant.
Without quality, privacy, and accountability baked into governance, even the most advanced AI won’t gain trust, or deliver real, sustained growth.
3. Security & compliance
Building in-house also means owning every layer of responsibility. If your AI leaks sensitive data, hallucinates into non-compliance, or violates privacy rules, there’s no vendor to call. It’s on you.
That means enterprises have to engineer:
- Pre- and post-processing checks on every LLM call to prevent leakage and enforce safety.
- Strict access controls. Agents must enforce entitlements, only serving data a user is entitled to. In other words, no employee should be able to query data they’re not authorized to see.
- Complete audit trails. Every output must be traceable back to a source, with citations to counter hallucinations.
- Regulatory alignment. GDPR, SOC 2, HIPAA compliance requirements don’t pause just because you’re experimenting with AI.
- Testing, evaluation, and rollback. Establishing guardrails and safe paths so that bad outputs or failures don’t go live.
- Versioning and documentation. Ensuring AI behavior can be tracked, updated, and explained
According to IDC, without proactive governance, enterprises risk non-compliance, reputational damage, and increased exposure to AI-driven threats, “To mitigate these risks and build trust in AI-powered security, organizations must establish a robust governance framework that ensures transparency, compliance, and operational resilience.”
4. Time & investment
Most in-house projects take 18–36 months before they’re usable in production. Costs often land between $5M–$20M, not including the hundreds of engineers required across AI ops, data pipelines, integrations, and UX.
Even then, operational overhead – monitoring, retraining, fixing drift, maintaining compliance – introduces ongoing costs that escalate with scale. Even if your AI agent reduces support interaction costs by $5–6 each, you’d need to resolve tens of thousands of tickets monthly just to offset a multi-million dollar investment.
5. User experience
A report by Nielsen Norman Group found that many current tools fail to meaningfully improve UX workflows, suggesting that poor usability in enterprise tools is still widespread. An agent that’s confusing, slow, or clunky won’t be used, no matter how advanced the model behind it.
Meeting these expectations adds more operational complexity. AI agents must:
- Handle richer, multi-modal inputs and outputs, including files, images, voice, and video
- Provide a consistent experience across chat, email, mobile apps, and social platforms
- Maintain reliable performance with low latency, even under heavy load
As I often remind teams: Users don’t care how many GPUs you deploy. They care whether the agent feels as fluid as the apps they use daily.
6. Scaling
The first agent is easy. The second is harder. By the time you have dozens, you’re running a distributed system of reasoning engines. Each agent needs to share context, coordinate tasks, and recover gracefully from failures. Without the right orchestration, a single weak link can ripple across the whole network.
To scale with multiplied complexity, a strong AI-native platform requires:
- Model versioning & evaluation. Model versioning tracks the modifications made to a model. These changes can be attributed to optimization efforts, changes in training data, etc.Every time the AI model gets updated, you need a way to test if the new version is actually better than the last one. Without that safety net, you risk shipping an “upgrade” that breaks accuracy or trust.
- Streaming inference for real-time performance. Most demos look fast because they’re built in controlled conditions. In production, speed matters even more as customers won’t wait 10 seconds for an answer. You need to deliver responses in real-time, like a conversation, and it requires heavy backend optimization and orchestration to do well at scale.
- Personalization, authentication, authorization. Tailoring answers to each user while respecting authentication and authorization.
- Heterogeneous data sources. With multiple agents, you need to pull from many heterogeneous sources such as transactional data, time-series, analytics, CRM, product logs, helpdesks, and more
This is where multi-agent orchestration becomes a systems engineering challenge. And it’s why, according to the 2025 MIT NANDA Report, purchasing vendor solutions succeeded 67% of the time versus just 33% for internal builds.
7. Continuous maintenance
Traditional software has a finish line: you ship version 1.0, then release updates on a predictable cadence. AI doesn’t work that way.
Agents drift. Their performance degrades as data changes, user expectations evolve, and new edge cases surface. Left unattended, yesterday’s accurate agent can quickly turn into a source of errors.
That’s why building in-house means creating an AI Ops discipline, not just an MLOps pipeline. Here’s what it takes:
- Versioning & evaluation pipelines. Every new model must be tested against the old one to prove it’s actually better.
- Feedback loops. Errors need to be captured, triaged, and retrained continuously.
- Rollback mechanisms Enterprises can’t afford rogue agents in production.
- A/B testing for reasoning. It’s not just features being tested anymore, it’s strategies of reasoning.
This treadmill never stops. Each new agent multiplies monitoring requirements, increasing the load on already scarce AI ops talent. No wonder Gartner predicts that 40% of agentic AI projects will be canceled by 2027 due to cost and complexity.
Final thought: focus on experience, not infrastructure
Building your own AI stack in 2025 is like building your own cloud in 2012. Possible, but costly, distracting, and rarely a competitive advantage.
Partnering doesn’t mean losing control. It means keeping your teams focused on what makes you special while leveraging a platform that evolves with every LLM release, orchestration standard, and interface innovation.
At DevRev, we can help you:
- Build proprietary skills, workflows, and UI that matter to customers.
- Drive differentiated value from your data and processes.
- Stay ahead with a platform that absorbs infra complexity so you don’t have to.
This means you can leverage our platform which evolves with every LLM release, every abstraction layer, and every interface innovation
Want to know more? Get in touch with us.
