Ticket triage and routing
A flow that classifies incoming requests, asks for missing information, routes to the right team, and records both the decision and its reasoning.
AGENT / 02
AI Agents & Intelligent Automation
We design agents not as chatbots, but as operating components with explicit permissions, tools, decision boundaries, and failure-recovery plans.
Discuss your project- 01Trigger
- 02Planning
- 03Tool call
- 04Human approval
- 05Action and log
The problem
We define the problem before the solution.
In demos, an agent is usually a chatbot. In production, an agent is a component that acts inside a system: it opens tickets, updates records, sends email, calls APIs. That makes the real design question not "how smart is the model" but "what is this component allowed to do, and what happens when it is wrong."
We build agent systems with explicit permission scope, tool contracts, approval points, and compensating actions. Irreversible operations are gated behind human approval, every step is traced, and failures are classified and measured. The value of automation shows up not in the runs that succeed, but in knowing in advance what happens in the runs that do not.
Use cases
Where this system usually shows up.
Back-office operations
An agent that performs repetitive steps such as record updates, reconciliation, and data entry under controlled permissions, pausing for approval at critical points.
Reporting and data-gathering flows
A flow that gathers and cross-checks data from several systems, flags inconsistencies, and presents the result alongside its sources.
Software delivery automation
A pipeline that reads an issue from the tracker, generates and runs tests, reports results back, and submits code changes for human review.
What does the system deliver?
Measurable automation of repetitive work
Safe decisions with human approval
Fully traceable agent operations
Delivery scope
We deliver an operable product, not just a model.
- 01
Workflow and automation opportunity analysis
- 02
Tool and permission architecture
- 03
Human-in-the-loop decision points
- 04
Tracing, evaluation, and security controls
How we build it
We design technical components together with the operating workflow.
Map the workflow
We make decisions, tools, exceptions, and ownership explicit.
Define boundaries
We define what the agent may do and when approval is required.
Connect tools
We connect CRM, ERP, email, tickets, and internal APIs with controls.
Observe and improve
We trace every step, classify failures, and continuously improve performance.
Technical approach
The layers and tools we work with.
- Orchestration
- LangGraph, MCP, and Temporal-based workflows
- Tools
- REST/GraphQL APIs, databases, business-software adapters
- Controls
- Narrow permission scope, dry runs, idempotency, human approval
- Observability
- Langfuse tracing, failure classification, evaluation sets
Frequently asked questions
What to know before making a decision.
01What happens if an agent performs an incorrect action?+
We limit impact with narrow permission scopes, human approval, dry runs, and idempotency. Rollback is used only when the connected system supports it; irreversible side effects require pre-action approval and designed compensating actions.
02Can it connect to our existing software?+
It can connect directly to systems with APIs, or through secure adapters and controlled RPA layers.
03How is an agent different from classic workflow automation?+
Classic automation is cheaper, faster, and more predictable when the steps and conditions are known in advance. An agent earns its place where input is free text, the order of steps depends on the situation, and context has to be interpreted before deciding. If the process is deterministic, we recommend classic automation.
04How do you keep the cost under control?+
Token use and latency are visible per step through tracing. Model choice is made step by step: simple classification runs on small models, and only the steps that need it reach large ones. Results are cached, and step and budget limits guard against runaway loops.
05Which processes are a poor fit for an agent?+
Processes where an error is costly and irreversible, where regulation requires a human decision, or where input is already structured and the rules are fixed are a poor fit. For those we recommend either classic automation or a design where the system only proposes and a person decides.
AGENT / 02
Let’s adapt this system to your operations.
We will assess your use case, data readiness, and integration requirements together.
Book a technical call- Direct email
- info@valnox.ai
- Location
- Bilişim Vadisi, Gebze/Kocaeli, Türkiye
- Delivery model
- Founder-led, end-to-end