Structured Workflows Over Prompt Engineering: How Kalarit.ai Designs Agent Logic

🐛 The Debugging Problem

Here's a scenario every AI team has faced: an agent gives a wrong answer, and no one can explain why. The prompt is a 2,000-word instruction set. The model's reasoning is opaque. The only debugging strategy is to tweak the prompt, redeploy, and hope.

Prompt-only agent design has a fundamental problem — it's a black box wrapped in natural language. When things go wrong (and they will), there's no structured way to trace the failure, isolate the cause, or prevent recurrence.

At Kalarit.ai, we took a different path. We design agent logic as structured workflow graphs — explicit, visual representations of how an agent moves from input to output. Every decision point is a node. Every transition has conditions. Every path is traceable.

🗺️ What Are Workflow Graphs?

A workflow graph models an agent's behavior as a directed graph where:

• Nodes represent actions, decisions, or states (classify intent, check database, generate response, escalate)
• Edges represent transitions between states, with explicit conditions (urgency = high, confidence < threshold, scope = out-of-bounds)
• Subgraphs encapsulate reusable patterns (the "gather context" subgraph works the same whether it's triggered by a tenant or a buyer)

This isn't just documentation — it's the actual execution model. The agent traverses the graph at runtime, and every step is logged. When something goes wrong, we don't guess. We look at the traversal path and see exactly where the agent diverged from expected behavior.

🏠 Real Estate Example: Maintenance Request

Let's walk through how a Kalarit agent handles a tenant maintenance request using a workflow graph:

Node 1: Intake The tenant submits a request. The graph captures the raw input and extracts structured fields (property ID, unit number, description).

Node 2: Classification The agent classifies the request type (plumbing, electrical, HVAC, structural, appliance) and urgency (emergency, urgent, routine). This node uses both keyword rules and an ML classifier — with the rules having override authority for safety-critical keywords.

Node 3: Lease Check The graph routes to a lease lookup node that checks whether the repair falls under landlord or tenant responsibility per the lease terms.

Node 4: Vendor Selection For landlord-responsible repairs, the agent queries the vendor database filtered by repair type, availability, and property location. It selects the best match and proposes a time window.

Node 5: Tenant Confirmation The agent presents the plan to the tenant — what's being repaired, who's coming, and when. The tenant confirms or requests changes.

Node 6: Dispatch & Logging On confirmation, the agent dispatches the vendor, updates the property management system, and logs the full interaction.

Escalation Edge: At any node, if confidence drops below threshold or the request falls outside the graph's scope, the agent escalates to a human with the full traversal context — not just a transcript, but which nodes were visited, what decisions were made, and where the process stalled.

⚡ Why Structured Beats Prompt-Only

	Prompt-Only	Workflow Graphs
Debugging	Rewrite the prompt and hope	Trace the exact node where behavior diverged
Auditability	Read a 2,000-word prompt	Inspect a visual graph with logged traversals
Maintenance	Change one thing, break three others	Modify a node without affecting unrelated paths
Testing	Run the whole agent end-to-end	Test individual nodes and edges in isolation
Onboarding	Read the prompt and figure it out	Read the graph and understand the flow

This doesn't mean we avoid language models. Our agents use LLMs extensively — for classification, response generation, and contextual reasoning. But the LLM operates within the graph, not as a replacement for it. The graph provides structure; the LLM provides intelligence within that structure.

🔧 How We Build Agent Workflows

Our workflow design process at Kalarit follows a repeatable pattern:

1. Map the domain — catalog every scenario the agent needs to handle, including edge cases and failure modes
2. Design the graph — define nodes, edges, and conditions. Start with the happy path, then add error handling and escalation branches
3. Implement node logic — each node gets its own implementation: some are deterministic rules, some are ML classifiers, some are LLM calls
4. Test in isolation — every node and edge is testable independently. We validate that the classification node handles ambiguous inputs, that the vendor selection node respects availability constraints, and that escalation triggers at the right thresholds
5. Integrate with funneling — the workflow graph plugs into our funneling pattern, ensuring every conversation follows the structured path from intake to resolution

🌳 Evolving with Generation Tree

Workflow graphs aren't static. As we collect data from production interactions, we use our Generation Tree framework to evolve them. A node that classifies with 85% accuracy gets retrained. An edge condition that triggers too many false escalations gets refined. The graph grows smarter without losing its structural integrity.

This is the key advantage: prompt-only systems evolve by rewriting prompts and crossing fingers. Graph-based systems evolve by improving specific nodes with measurable, isolated changes.

Ready to build agents with workflows you can actually debug? Contact us to learn how Kalarit.ai designs structured agent logic for real estate.

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