At Adict, we're not just wrapping LLMs in UI. We're building Agentic Workflows—systems that can think, plan, and execute multi-step business processes with precision.
As an engineer on the team, my focus has been on ensuring these agents aren't just fast, but reliable. Here's a deeper look into the principles that power our agentic systems.
The Shift from Linear to Agentic
Fig 1: Abstract visualization of the iterative Reasoning Loop: Analysis, Planning, Execution, and Feedback.
Traditional automation is linear: if X, then Y. While effective for simple tasks, this model breaks down in dynamic business environments. Our agentic approach introduces a layer of Autonomous Reasoners that can handle ambiguity.
Rather than a static script, our agents operate on a cycle of observation and action. When a user asks to "Draft an invoice for Acme Corp based on last month's deal," the system doesn't just run a database query. It analyzes the intent, identifies missing information, and iteratively gathers the necessary context before ever touching the billing engine.
Strategic Planning & Tool Use
The core of our agentic workflow is the Planning Module. When an objective is received, the agent generates a high-level plan. This plan isn't a single command but a sequence of actions that might involve:
- Querying the CRM for deal history.
- Analyzing past invoice patterns for the specific client.
- Validating line items against internal pricing rules.
Each of these steps is executed using specialized tools. By decoupling the "reasoning" from the "doing," we allow the agent to adapt if one of the steps fails or returns unexpected data.
Self-Correction and Verification
One of the most powerful aspects of our system is its ability to verify its own work. Before the agent presents a result to the user, it runs a Verification Pass. It asks itself: "Does this invoice match the deal terms I found in step one?"
If there's a discrepancy, the agent can re-run specific parts of the process, self-correcting without user intervention. This significantly reduces the error rate compared to standard automated systems.
Deterministic Safety Nets
Fig 2: The Core-Shell Architecture: A flexible AI core protected by a rigid, deterministic safety envelope.
While the agent's logic is flexible, its actions are governed by strict safety protocols. Every interaction with the database or third-party APIs is wrapped in a deterministic layer. We ensure that while the agent can plan complex operations, it can only execute them within predefined, safe boundaries.
The Road Ahead
We're currently evolving our "World Model"—the agent's understanding of your business structure. The goal is to move beyond reacting to prompts and start proactively identifying opportunities for automation across your entire sales cycle.
Building these systems requires a balance of innovative AI research and rigorous engineering. We're excited to be at the forefront of this shift towards truly agentic business operations.
— Owais, Software Engineer at Adict