Building AI-Assisted Workflows in Enterprise Applications

Agentic AI systems differ fundamentally from traditional AI integrations. Instead of a single prompt-response cycle, an agentic system plans multi-step workflows, uses tools to retrieve data or execute actions, evaluates intermediate results, and iterates toward a goal. Building these systems in enterprise OutSystems applications requires new architectural patterns and careful attention to observability and control.

The Agent Executor Pattern structures the core agentic loop. Your OutSystems application hosts an Agent Server Action that accepts a goal and a tool registry. The action sends the goal to an LLM, receives a plan (often expressed as tool calls), executes the requested tools, returns results to the LLM, and repeats until the LLM signals completion. This loop must have a maximum iteration count and timeout to prevent runaway execution.

Tool design is the most critical architectural decision in agentic systems. Each tool is an OutSystems server action exposed to the agent. Tools must have single, clear responsibilities. A QueryCustomerBalance tool, a CheckInventory tool, a SubmitPurchaseOrder tool. Ambiguous or multi-purpose tools cause agent confusion and unpredictable behavior. Document each tool with a precise description — the LLM uses this description to decide when to call it.

Human-in-the-loop checkpoints prevent autonomous agents from taking irreversible actions without oversight. Before the agent executes a high-stakes tool (submitting an order, modifying account data, sending external communications), pause the workflow and present a confirmation step to the user. OutSystems' BPT human tasks provide exactly this pattern — the workflow suspends, a task is assigned to a user, and execution resumes only after explicit approval.

Observability infrastructure separates production agentic systems from demos. Log every LLM call, every tool invocation, every intermediate reasoning step in a dedicated AgentExecutionTrace entity. Include timestamps, token counts, tool inputs and outputs, and any error conditions. This trace enables debugging, cost analysis, and audit compliance — requirements that enterprise systems cannot bypass.

Failure mode design is non-negotiable. Agents can enter loops, produce malformed tool calls, exceed context limits, or receive API errors. Each failure mode requires an explicit handling strategy. A maximum retry count per tool. A fallback prompt when tool calls fail parsing. A circuit breaker that suspends the agent and notifies a human operator when error rates exceed threshold. Resilient agentic systems are designed for failure, not just for the happy path.