The artificial intelligence landscape is evolving at a breakneck pace, and nowhere is this more apparent than in the emerging field of agentic AI. As companies rush to build and deploy intelligent, distributed systems, the conversation is shifting from what’s possible to what’s reliable and trustworthy.
But in the midst of this shift, we’re also seeing a familiar pattern of speculative frenzy—a “bubble that knows it’s a bubble,” as Craig McCaskill aptly puts it. Valuations are soaring far beyond the fundamentals of revenue or, more critically, proven longevity.
Take LangChain. Experimentation with it for early projects has been significant. A recent report suggests a $1B+ valuation—with annual recurring revenue (ARR) estimated at less than $15 million. A 66x multiple? 🤔 Based on the history of other tectonic technology shifts, this kind of disconnect between market hype and production results should give every enterprise leader pause.
Recent community discussions about LangChain suggest many have already paused:
Early data from the broader market shows a clear need to separate hype from results:
This isn’t failure of imagination—it’s failure of infrastructure engineering. These statistics don’t indicate that AI is dead. Not even close. They show that most agentic projects today are experiments, not hardened systems. That’s the real gap. The hype is meaningful. The deployments are not.
What makes agentic AI—systems that can plan and act autonomously—attractive, is also what makes it fundamentally complex to operate at scale. These systems are not just stateless function calls or chained APIs. They are stateful, long-running, concurrent processes that act autonomously in partially observable environments.
That puts them squarely in the domain of distributed systems.
And they introduce new layers of complexity that go beyond conventional cloud architectures:
This isn’t just “microservices, but smarter.” It’s a fundamentally harder problem domain.
Agentic systems require you to reason about durability, observability, backpressure, concurrency, state propagation, and scheduling, all while considering probabilities, predictions, and real-world side effects. You don’t get this for free from an LLM. And you certainly don’t get it from a new orchestration library.
Agentic systems are distributed systems, but with failure modes and complexity classes most teams haven’t had to routinely deal with before.
LangGraph, a LangChain product, is one of the frameworks in the agentic AI ecosystem. It allows developers to define agent workflows as graphs, combining memory, tools, branching logic, and control flow into a single execution model.
But, it’s important to understand what LangGraph really is: a durable execution engine.
That means it’s implicitly responsible for coordinating distributed state across time, machines, and failures. It must ensure that when a step is defined, it will eventually be executed, exactly once, in the right order, with the right context, even in the face of failure.
This is a fundamentally hard class of system to build. Durable execution engines are not just function orchestrators. They are distributed systems that need to account for persistence, recovery, idempotency, retries, and external side effects.
We’ve seen this exact challenge play out before:
These systems didn’t fall short because they were poorly designed. They fell short because distributed systems are adversarial by nature. Edge cases happen constantly, and the only way to build confidence is by surviving them in production.
LangGraph hasn’t gone through that phase yet. It’s early, evolving, and promising, but as with all systems in this category, the abstractions are only as trustworthy as the runtime beneath them.
Durable execution is a commitment, not a feature or an add-on product. And in distributed systems, every commitment comes with a liability: to state, to failure handling, to correctness. You don’t get those guarantees by writing a YAML spec or wrapping a call in a try/except block. You get them by building infrastructure that has failed—and been fixed—at scale.
Over the past 15 years, Akka has helped companies build distributed systems that operate reliably at scale. The systems powered by Akka don’t just serve dashboards or call APIs. They detect fraud, manage payments, route logistics, process petabytes of data, and keep global infrastructure running. Some of our customers have achieved more than a decade of uninterrupted uptime.
That didn’t happen by chance. It came from solving real operational problems like:
This wasn’t theoretical. Each architectural choice came from a production incident or a scaling constraint. Real infrastructure evolves with postmortems, not design documents.
When we talk to executives and engineering teams building agentic systems, their goals aren’t philosophical, they’re operational. And across industries, the priorities are consistent:
These are the outcomes that define success for agentic systems in the enterprise. And they’re the reason most projects fail to cross the gap from prototype to production. Moreover, this is the basis for why experts in the trenches with enterprise customers claim, “Quite simply, Akka provides the industry’s best way to build agentic AI systems that scale in the enterprise and ensure stability, performance, and outcomes.”
The promise of agentic AI is real, but the technology to deliver it must be built on a foundation of stability, trust, and proven performance. In a hyped up market where a vendor's valuation can balloon (and evaporate) overnight, it is essential to choose a partner that has been building the future of distributed systems for more than a decade, not just riding the current wave.