Loading
0%
LangChain · Claude Tool Use · RAG
AdwebX builds task-specific AI agents with tool use, RAG, and guardrail architecture. Auditable, secure autonomous assistants that connect to your CRM, email, and internal systems.
AI agents differ from one-off prompts: they use tools, retain memory, and complete multi-step tasks. We deploy them production-ready with security guardrails and human-approval layers.
Every month you wait, the gap between you and competitors widens.
Without a 24/7 agent, 40% of customer inquiries and leads go unanswered outside business hours.
Human-gated workflows average 3 business days per decision — by then, conversion interest has cooled.
Your competitor's AI agent qualifies and logs a lead in 2 minutes; your team follows up 2 days later.
Real business outcomes with industry-standard tools.
The word agent is used widely in the AI world, but its meaning often remains vague. The precise definition: an AI agent is not limited to a single input-output cycle. It uses tools (web search, API calls, file reading), retains memory (recalls previous steps), plans and completes multi-step tasks.
Summarise this document is a prompt. Scan incoming emails for contract requests, flag non-standard clauses, route a summary to the relevant legal team and track response time — that is an agent task.
Agents that can search the web, pull data from specified sources, structure raw information and produce output in a defined format. For content teams, research departments and product-development workflows.
Agents that update CRM records, assign tasks, extract action items from meeting notes and send follow-up emails. They redirect the time allocated to sales and customer-success teams towards high-value work.
Agents that query a database or external systems at defined intervals, detect anomalies, format a report and deliver it to relevant stakeholders. For recurring reporting cycles that require no human intervention.
Agents that take over complex requests the chatbot cannot resolve, query back-end systems (order status, account information, technical records), prepare a response and, where necessary, hand it over to the live team with a summary.
Autonomous system talk always brings a legitimate question: what if it does something wrong? We take this question seriously. Every agent architecture we build includes three safety layers:
For an agent to produce reliable responses, it needs access to accurate information. Retrieval-Augmented Generation (RAG) lets the agent query a real knowledge base (company documents, product catalogue, policy documents, support history) before invoking the generative model.
This solves two problems: model hallucination is reduced because the response is grounded in a real source document; and knowledge stays current because the document base can be updated without retraining the model.
Agent deployment begins with a clear task definition: what the agent will do, which systems it will access, and which decisions it will defer to human approval. Without answers to those three questions, no agent architecture can be built — and we clarify those answers together in the first conversation. Fill in the form at /en/analysis for a free assessment, or message us on WhatsApp.
RPA replicates defined steps on a screen; it is rule-based and brittle. An AI agent can work with ambiguous, natural-language inputs, interprets context and chooses different paths for different situations. The two can be complementary; they are not interchangeable.
Every agent we deploy logs its processing steps. These logs are sent to a defined dashboard or notification channel (Slack, email). An alert is triggered when abnormal behaviour is detected.
The agent can only read from or write to the permitted tables and fields; this boundary is defined at the technical level. Authentication, API key management and access logging are part of the standard architecture.
In most cases a production-quality commercial model (GPT-4o, Claude Sonnet) is used; where cost or data-privacy requirements apply, self-hosted open-source models (Llama, Mistral) are considered. Selection is made based on the use case.
Yes. A multi-agent architecture allows different agents for different tasks to work in coordination. For example, a research agent can collect data and pass it to a reporting agent. As this architecture grows in complexity, the orchestration layer becomes increasingly critical.
Pilot agent projects range from ₺20,000 to ₺60,000; production-grade autonomous systems start at ₺100,000+. We clarify the exact scope in your free call.
We don't publish fixed price lists — quoting without scoping the project doesn't serve you. A free discovery call lets us understand your needs and present a project-specific proposal.
Let's define the project-specific investment together in a free discovery call.