Everyone is talking about AI agents. But building one that actually works in production is a different story. At The AI Factory, every agentic project starts with the same question: what problem does it have to solve? Not which model to use, or how many tools to connect. The problem comes first. Everything else follows from there.
What "agentic" actually means
An agentic system is not a chatbot. It's not a workflow with an LLM bolted on top. A truly agentic system has three properties: it observes its environment continuously, it reasons about what it finds, and it acts without waiting for a human to press a button.
We treat LLMs as probabilistic oracles: useful, but never unconditionally trusted. An agentic system observes its environment, makes decisions based on what it sees, and takes action, or escalates to a human, but always with structured guardrails.
The architecture of a production agent
Every reliable agentic system we've deployed shares the same core architecture:
Data ingestion layer
Continuously collects data from external sources. APIs, web scrapers, document pipelines, RSS feeds, database changes. This layer runs on a schedule or event-driven triggers.
Intelligence engine
Processes incoming data against user-defined criteria. This is where the LLM lives, but always with structured outputs, retry logic, and fallback mechanisms. Never trust an LLM to be reliable without guardrails.
Action layer
Triggered by the intelligence engine's decisions. Actions range from sending alerts and generating reports to triggering API calls, updating dashboards, or escalating to humans.
Feedback loop
Users can flag false positives, adjust thresholds, and refine the agent's behavior over time. Without this, agents drift and users lose trust.
Why most agentic systems fail
Getting an LLM to do something impressive in a demo takes an afternoon. Getting it to do something reliable in production takes months. The gap between demo and production is the hardest engineering challenge, and it's not an AI problem, it's a product engineering problem.
What happens when a data source is unavailable, overloaded, or returning nonsense? The demo agent crashes. The production agent retries, falls back to cached data, logs the incident, and continues with degraded but functional output.
Alert fatigue. An agent that produces too many false positives is worse than no agent at all. Users disable it, ignore it, or resent it. Getting the signal-to-noise ratio right is the hardest engineering challenge, and it's not an AI problem, it's a product design problem.
Real examples from our work
We build and deploy agentic systems that observe, decide, and act on behalf of your organization, from alerting systems to fully automated document processing pipelines.
For crisis communication partner FirstRing, we built agents that continuously scan incoming information during crises, generate situational assessments, and draft response plans, giving communication teams a critical head start when minutes matter.
When to build an agent (and when not to)
Agentic systems make sense when: the data changes frequently, timely response matters, and the task is too repetitive or too high-volume for a human team to manage manually.
They don't make sense when: the task requires nuanced judgment that can't tolerate errors, the data environment is static, or the cost of a wrong action is higher than the cost of a delayed human decision.
The best approach is often hybrid: let the agent do the monitoring and preliminary analysis, then escalate to a human for final decisions on high-stakes actions.
The most important question: what problem does it have to solve?
Our conversation with clients always starts with one question: what problem does this system need to solve? Not which framework to use, not which model to pick. The problem definition drives everything. From there, we design and build. We handle the architecture, the integrations, the edge cases, and the production hardening. You focus on what you know best: your domain.
The beauty of agentic systems is how naturally they grow. Once the core architecture is in place, adding new data sources, new action types, or new user workflows is straightforward. A system that starts as a simple alert engine can evolve into a full autonomous decision-support platform, one capability at a time. That modular expandability is not an accident. It is a design principle we build into every agent from day one.
The interface is just as important as the model behind it. A good sync between human and machine requires an interface designed for the way people actually work. We build custom interfaces tailored to your team and your workflow, so the AI feels like a natural extension of your process rather than a separate tool that demands attention.
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