AI pilots look promising until they hit production. When they fail, it usually has nothing to do with intelligence.
A revenue ops team asks their AI agent to analyze pipeline health. It needs to pull deal data from Salesforce, enrich it with engagement metrics from the analytics warehouse, cross-reference budget constraints from finance, and generate an updated forecast. Does this sound like too much at once? Considering how advanced LLM models have become, this should be a breeze.
And indeed, the agent starts strong. Until it hits an OAuth wall accessing Salesforce. So it pulls stale data from a disconnected analytics tool. And by the end of the analysis, it forgets the budget constraint it identified at the beginning. The forecast it produces looks complete, but no one can act on it: a simple human check reveals there is obviously something wrong with it.
So, the team assumes they need a smarter model. Which sounds logical, but what they're in fact seeing is AI agents trying to operate without the infrastructure every distributed system needs.
Sure, the demo worked great. But, it was only because someone manually set up credentials, pre-loaded context, and kept it simple. In real-world circumstances, production breaks because real workflows are complex, credentials expire, and context drifts.
Is this an intelligence problem? No, it's an infrastructure problem.
The Pattern Behind the Failures
When enterprises scale AI agents beyond pilots, they hit two failure modes: agents can't access the systems they need, and agents can't maintain coherent reasoning across workflows.
Data fragmentation keeps agents from reaching information scattered across platforms. Context drift means agents forget what they learned earlier or make contradictory decisions. Most organizations treat these as separate problems requiring separate solutions.
They're not. Both stem from the same architectural gap: AI agents are operating without the foundational primitives, the core building blocks every distributed system relies on. Traditional infrastructure was built for humans with stable credentials and persistent sessions. AI agents are ephemeral processes making hundreds of calls per minute across shifting contexts.
What's actually missing: persistent identity, governed access, durable memory, and deterministic policy enforcement. Remove any one, and autonomy collapses.
Why the Obvious Fixes Don't Work
The natural response is patching with existing tools.
RAG (Retrieval-Augmented Generation) retrieves facts from past workflows, but not the current reasoning chain. When the agent needs to know whether it has already checked a supplier, RAG returns documents about compliance procedures, not the specific check from five minutes ago.
Vector databases store embeddings for semantic search, but can't enforce deterministic logic or maintain structured state across workflow steps.
API gateways organize traffic without providing identity, memory, or governance. They route requests without answering who's making the request or how it relates to previous actions.
Traditional IAM (Identity and Access Management) was built for humans logging in once a day with fixed roles. It's functionally obsolete for agents that need permissions expanding and contracting hundreds of times per minute.
Fine-tuning through training changes model behavior but doesn't provide memory, identity, or governed access. Longer context windows just make the problem more expensive.
You can't integrate your way out of an infrastructure gap.
What Agents Actually Need
1. Identity that persists beyond a single session
Currently, AI agents use temporary credentials, if any. When one needs to access Salesforce, then the data warehouse, then compliance systems, there are two equally bad options: it either operates with dangerous blanket permissions or it gets blocked at every gate.
Without verifiable identity, IT teams resort to shared service accounts (completely unauditable), hardcoded API keys (impossible to revoke safely), or manual approvals (eliminating autonomy entirely).
2. Access that works across fragmented systems
Even with identity, agents still face a different authentication method for every system. OAuth for SaaS platforms. VPN tunnels for legacy systems. API keys for data warehouses. Each one expires on its own schedule and fails in its own way.
The agent building a forecast shouldn't manage OAuth flows, credential rotation, or authentication failures across twelve systems. It should present verified identity once and receive temporary, scoped access to what it needs.
3. Memory that survives the workflow
LLMs are stateless, in other words, they start fresh every time. Every decision, tool call, or data retrieval lives only in a temporary context window that gets wiped. As workflows extend, those windows fill with logs and responses.
The agent that identified a budget constraint in step five has no record of it by step fifty. It either re-runs the analysis (expensive) or proceeds without it (unreliable). When multiple agents coordinate, a task costing a single agent $0.10 can cost a multi-agent system $1.50, most of it going to context sharing and state reconstruction.
Durable memory means storing verified facts and workflow state outside the LLM's context window. Shared state means multiple agents don't duplicate work or produce conflicting outputs.
4. Policy enforcement that actually works
LLMs reason probabilistically as they try to be helpful. But authorization decisions can't be probabilistic: an agent either has permission to access financial data or it doesn't.
Without external policy enforcement, agents are vulnerable to prompt injection and contaminated context. A malicious PDF can include text that looks like a comment to humans but gets interpreted as a command by the agent. Traditional role-based access control can't prevent this because it operates outside the agent's execution path.
Production systems need deterministic guardrails: hard rules enforced as code, not suggestions embedded in prompts. This is why policy must be enforced outside the LLM's reasoning process, where it can't be bypassed or reinterpreted.
These four primitives can't exist as separate tools. They need to work together in a unified layer.
Where the Industry Is Heading
The convergence is visible. The Model Context Protocol (MCP) standardizes how LLMs integrate with external tools and how context should be managed outside the model. Open Agent Architecture (OAA) frameworks define formal specifications for agent communication and state management.
Major AI platforms are adding persistent memory layers, identity systems, and policy engines. Enterprises are demanding auditable agent actions with deterministic enforcement. Regulatory frameworks are starting to require provenance tracking for automated decisions.
This pattern mirrors every platform shift. The web needed HTTP, DNS, and TLS. Mobile apps needed identity federation and permission systems. Cloud computing needed IAM and encryption before regulated industries would adopt it.
AI agents need their own infrastructure layer. The shape is becoming clear because the requirements are non-negotiable: identity, access, memory, and governance working together.
Building on Infrastructure, Not Patches
The problems that kill AI productivity have nothing to do with model intelligence. The breakthroughs ahead won't come from models with million-token context windows or reasoning that's 2% more accurate. They'll come from infrastructure that gives models the ability to operate safely and reliably in production environments.
The architecture is becoming standardized. Platforms are implementing this unified layer. The question facing enterprises isn't whether this infrastructure is necessary (it clearly is) but how quickly they can move past workarounds and build on proper foundations.
