What Agentic Software Really Means: Beyond LLM Wrappers and Hype
Agentic software is coming. But it won’t look like sci-fi. It will look like well-engineered systems with LLMs in the loop. And that’s how it should be.
In the rush to build the future of intelligent systems, "agentic software" has become one of those magnetic terms that pulls in founders, engineers, and investors alike. It's got the right mix of buzz and ambition. But somewhere along the way, the core idea has been diluted—flattened into mere LLM wrappers, code interpreters with a fancy name, or brittle demo bots talking to each other in a shell script. It's time to re-anchor the conversation.
Agentic Software Is Not a Chatbot with Loops
At its core, agentic software is not about talking agents coordinating via APIs. It’s not about spawning "worker" LLMs under a “manager” LLM. It’s about orchestration but with a twist.
Traditional orchestration systems like Airflow, Kubernetes, or Zapier operate on static logic: a directed acyclic graph of tasks, a sequence of conditions, a set of rules. Agentic systems inject a degree of autonomy—decisions that can't be hardcoded in advance because the information landscape is too messy, too unstructured, too human. Enter LLMs.
The LLM’s Role: A Local Decision-Maker, Not a Global Brain
Think of the LLM as a localized decision engine, not the operating system. You might use it to evaluate a customer message and decide if it's a billing issue, a cancellation request, or a high-risk complaint. Or maybe it chooses whether a user's prompt deserves re-ranking, a follow-up question, or immediate escalation. These are local decisions—bounded in scope, non-catastrophic in failure.
This is crucial: LLMs make soft decisions. They must be reversible, inspectable, testable. If they hallucinate or misfire, the system needs scaffolding to catch it—unit tests, retry loops, heuristics, human escalation paths. You wouldn’t let an LLM manage your distributed cluster directly; you let it suggest, route, annotate. And even then, with a leash.
Recovery, Fallibility, and Ground Rules
True agentic software is built to recover. The moment you introduce probabilistic reasoning into an orchestration pipeline, you must introduce guardrails. Not just syntactic ones like schema checks or exception handlers, but architectural ones—decoupled control flows, validation checkpoints, audit logs.
Here’s what a production-grade agentic system does not do:
Depend on a single LLM to make irreversible financial decisions.
Assume the output of a generation step is correct.
Let tasks run indefinitely because no explicit failure mode was encoded.
Here’s what it does do:
Validate LLM decisions with traditional programmatic checks.
Track state transitions explicitly across memory or context windows.
Use LLMs for what they’re good at—weak reasoning over natural language, synthesis, heuristic classification—not precision, not logic, not arithmetic, not control.
It’s Not Autonomy, It’s Delegation
The word “agent” conjures images of autonomy, but agentic software is not autonomous in the strong sense. It's delegated intelligence within a larger deterministic system. The architecture is still engineered, still predictable. It has observable behavior, not emergent behavior. The agent is there to relieve you of brittle rule-writing, not system responsibility.
What many current frameworks do wrong—whether it’s CrewAI, Autogen, or similar—is that they prioritize “coordinated conversation” over actual systems design. Just because two LLMs can talk to each other doesn’t mean anything meaningful will happen. Dialogue is not logic. Message passing is not architecture. These demos are valuable, but they’re not production patterns.
Real Agentic Software Is Boring (and That’s Good)
The real work is in the boring stuff: setting up input contracts, routing logic, validating outputs, retrying failures, interpreting logs. The LLM doesn’t make that go away—it just lets you cover the last mile of fuzziness in an otherwise well-specified pipeline.
A proper agentic stack might include:
Deterministic routing logic.
LLM components acting as policy approximators.
A memory store with versioned state transitions.
Robust observability tooling (not just LangChain “traces”).
System-level tests that capture multi-step reasoning paths.
The Future Is Clearer Than the Hype
Agentic software is the natural evolution of workflow orchestration in a world where the edge of automation meets ambiguity. It’s not a revolution—it’s a convergence of deterministic systems with soft reasoning modules. And it needs to be treated with the same rigor we apply to any critical software infrastructure.
The key isn't to give agents more freedom. The key is to give systems more context-aware, recoverable, bounded judgment—and to design around that as a first-class constraint.
Agentic software is coming. But it won’t look like sci-fi. It will look like well-engineered systems with LLMs in the loop. And that’s how it should be.