Enterprise AI Agent Architecture: How to Build Action-Capable Assistants at Scale

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In today’s enterprises, simply answering questions isn’t enough. Modern enterprise AI agents need to both chat with users and act on their behalf to drive real outcomes. This pillar guide presents a five-layer architecture – essentially an enterprise-grade agent operating system – for building action-capable assistants at scale. From unified data connectors and retrieval-augmented generation to model orchestration and low-code automations, we’ll examine each layer with a business outcome focus. Klart AI’s approach brings together unified knowledge, models, and actions in one platform – a strategy already embraced by well known companies in the industry to transform productivity. Let’s explore how to move beyond “chat-only” bots to AI agents that deliver tangible results.

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Table of Contents

1. Why “Chat, then Act” Beats “Chat Only”
   
   
2. Layer 1 – Data & Tool Connectors
   
   
3. Layer 2 – Retrieval-Augmented Generation (Serverless)
   
   
4. Layer 3 – Model Router & Cost Control
   
   
5. Layer 4 – Low-Code Action Blocks
   
   
6. Layer 5 – Security, Governance & Observability
   
   
7. Reference Implementation (Klart AI Blueprint)
   
   
8. Case Study – 70% automation of Support Tickets
   
   
9. Build Checklist & ROI Calculator (Download)
   
   

Why “Chat, then Act” Beats “Chat Only”

Traditional chatbots are often glorified FAQ systems – they can retrieve information or answer basic questions, but they stop at chat. After getting an answer, an employee still has to switch apps, copy the info, and perform the task themselves. This disconnect between chat and action carries a hefty productivity cost. Analysts estimate that fragmented workflows and manual follow-ups can cost around €2,500 per employee per month in lost efficiency. Pure “chat-only” assistants might save a few minutes on searching for answers, but they don’t truly eliminate the work.

In contrast, a “Chat, then Act” agent follows through on the conversation. It not only provides information but also executes the next step – whether that’s booking a meeting, updating a record, or generating a report – all from within the chat interface. For example, instead of just telling a support engineer how to resolve an IT ticket, an action-capable agent can resolve it automatically. This closed-loop approach means employees and customers get what they need faster. The result is a direct boost to productivity and satisfaction: users spend less time on mundane tasks and more on high-value work. In short, chat+action agents turn talk into tangible outcomes, which is why they consistently beat chat-only bots in enterprise ROI.

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Layer 1 – Data & Tool Connectors

The foundation of any enterprise AI agent is access to the right data and tools. Layer 1 focuses on integrating the agent with your existing knowledge bases and applications. Klart AI provides a rich library of native data & tool connectors so your assistant can tap into information from across the organisation. Out-of-the-box integrations include:

• Google Drive
  
  
• Microsoft OneDrive & SharePoint
  
  
• Atlassian Confluence
  
  
• Notion
  
  
• Zendesk
  
  
• Zoomin / Salesforce
  
  
• Snowflake
  
  
• AWS S3 (Simple Storage Service)
  
  
• Google Cloud Storage (GCS)
  
  
• Azure Blob Storage
  
  
• Custom REST or GraphQL APIs
  
  

These connectors unify previously siloed knowledge sources into a single searchable index for the AI. That means the agent can securely read company wikis, policies, tickets, and even database records to answer questions with real data. No more bouncing between SharePoint, Salesforce, and other apps – the agent brings it all together. Connectors also enable the assistant to interact with tools (for example, fetching a file from Google Drive or pulling a customer record from Zendesk) as part of answering a query.

Beyond native plugins, the platform’s API connector allows you to link any bespoke system via RESTful or GraphQL endpoints. This flexibility ensures that even proprietary in-house databases can feed into the AI’s brain. By establishing Layer 1 connectors, you create a unified knowledge hub for your AI agent. All content stays current too – connectors sync data on schedule or in real-time, so the agent is always using up-to-date information.

Pro-Tip: Start by connecting your highest-value knowledge source first (e.g. an internal wiki or FAQs). This yields quick wins in answer quality and builds user trust. You can then gradually add more integrations – including custom APIs – to expand the assistant’s knowledge and capabilities.

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Layer 2 – Retrieval-Augmented Generation (Serverless)

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With data connected, the next step is making sure the AI can retrieve and use it effectively. Layer 2 is Klart AI’s proprietary Retrieval-Augmented Generation (RAG) engine – a serverless pipeline that supplies the language model with relevant enterprise data in real time. Here’s how it works: when a user asks a question, the system searches an in-house vector database of your documents (all encrypted for security) to find the most relevant snippets, and injects those into the prompt. The Large Language Model (LLM) then crafts its answer grounded by those retrieved facts. This ensures responses are factual, up-to-date, and drawn from your organisation’s knowledge.

Klart AI’s RAG layer is built for extreme performance and privacy. The retrieval process runs on a serverless architecture that scales on demand – there are no servers to manage, and 95% of queries return in under 1 second. All vector searches hit the encrypted database managed within Klart AI’s platform, so sensitive content never leaves your controlled environment. The RAG system is also multilingual: it supports retrieval across 50+ languages, meaning an employee can ask a question in French and get an answer from an English document (or vice versa) seamlessly. It even parses images and tables embedded in files, so nothing is missed just because information was locked in a chart or screenshot.

By fusing generative AI with real enterprise data, Layer 2 brings retrieval-augmented generation to enterprise scale. Your assistant stops guessing and starts knowing – if the answer lies in a PDF report or a database table, it will find it. This drastically reduces hallucinations and builds user confidence, since answers come with evidence from internal sources. Klart AI’s serverless RAG sets a new benchmark for retrieval-augmented generation in enterprise settings, combining speed and accuracy so that users get the right answer almost instantly every time.

Pro-Tip: Keep your knowledge base fresh. Establish a routine to index new or updated documents (daily or weekly). Up-to-date data in the vector store means your AI agent won’t cite stale information. A well-maintained RAG layer ensures the assistant’s answers are always current and credible.

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Layer 3 – Model Router & Cost Control

At the heart of the agent is the AI brain itself – but not one brain fits all situations. Layer 3 introduces a dynamic model router that can leverage different Large Language Models depending on the query, context, or cost considerations. Klart AI’s platform is model-agnostic, supporting a range of top-tier and open-source LLMs. For example, you can plug in models from OpenAI (like GPT-o4 or GPT-4.1), Anthropic (Claude 3.7), Google’s Gemini, Cohere, Perplexity Sonar, or Meta’s Llama-3. It also supports fast local models such as Mistral, and even allows self-hosted custom models via API. This extensive choice means you can tailor each agent to the model that best fits its purpose (and budget).

The per-agent model selection feature lets you set model preferences on a per-use-case basis. For instance, a customer-facing Slack assistant might default to GPT-4 for its fluency, whereas an internal IT agent could use a smaller open-source model to keep costs low. You can configure a primary model and a fallback: if the first model fails or times out, the router automatically retries with an alternative. This built-in failover ensures high reliability – users get answers even if one provider is slow or unavailable.

Critically, the model router is tuned for cost control. You can enforce policies so that simple queries use cheaper models, while complex ones invoke a more powerful LLM only when necessary. The system can even intelligently route based on confidence – try a fast, low-cost model first, and escalate to a top-tier model only if the initial answer isn’t confident. This smart orchestration minimises your AI API expenses without sacrificing quality. In effect, Layer 3 gives you the benefits of an ensemble of models: the right model for the right task, optimising both performance and spend.

Pro-Tip: Not every query needs an expensive model. Set your agent’s default to a cost-efficient LLM and reserve GPT-4.1 (or other premium models) for the hard cases. By tuning confidence thresholds and fallbacks, enterprises often slash their monthly LLM costs while maintaining excellent answer quality. It’s all about finding the right balance between speed, smarts, and spend.

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Layer 4 – Low-Code Action Blocks

Having information and answers is powerful – but executing tasks is game-changing. Layer 4 equips your AI agent with low-code action blocks, turning it into an active participant in workflows. These blocks are pre-built “skills” the assistant can use to perform operations, all configured with minimal coding. Out of the box, Klart AI provides actions for common enterprise tasks such as:

• Sending an email (e.g. to reply to a customer or notify a colleague)
  
  
• Posting a message to Slack or Microsoft Teams (the agent can become your Slack AI assistant, updating channels or answering queries where work happens)
  
  
• Triggering webhooks (to call external APIs or initiate processes in other systems)
  
  
• Running a Snowflake SQL query (making the assistant a Snowflake SQL agent that can fetch analytics or update the data warehouse)
  
  
• Executing multi-step workflows (chaining multiple actions with logic, like “if inventory is low, place an order then send a report”)
  
  

These actions can be combined and sequenced without heavy coding – typically through a visual interface or simple scripting. Your operations team can automate complex processes by having the AI orchestrate them, achieving what’s often called “zero-click ops.” Imagine an employee requests a software license in chat: the agent not only answers but also creates a ticket via webhook and sends a confirmation email, all in seconds and without any human clicks.

By embedding these operational capabilities, the AI assistant becomes a true agent, not just a chat companion. It can take initiative to complete tasks end-to-end. Each action is governed by rules and permissions (defined in Layer 5) to ensure safety – for example, you might allow the agent to auto-approve refunds up to a certain amount, but require a manager’s approval above that. The net effect is transformative: routine tasks get offloaded from humans to AI, processes speed up, and zero-click automation moves from concept to reality.

Pro-Tip: When rolling out action automations, start simple and safe. Enable the agent to handle low-risk, frequent tasks (like sending a status update) first. For higher-impact actions, introduce a human approval step initially. As the agent consistently performs well, you can remove approvals and fully embrace zero-click workflows with confidence.

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Layer 5 – Security, Governance & Observability

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Any enterprise-grade system must have guardrails and visibility, and AI agents are no exception. Layer 5 provides the security, governance & observability framework around your AI assistant deployment – crucial for compliance and trust. Klart AI was built with AI agent governance in mind, offering features that satisfy IT and InfoSec requirements:

• Single Sign-On (SSO) and SAML support for secure, centralized authentication
  
  
• SCIM integration for automated user and group provisioning
  
  
• Role-Based Access Control (RBAC) to restrict which users or groups can query certain data or execute particular actions
  
  
• Comprehensive audit logs of all queries and agent actions, for traceability and compliance
  
  
• Sensitive data handling like field-level redaction to mask PII or confidential info in prompts and responses
  
  
• Compliance certifications (e.g. SOC 2 Type II) and optional on-premises or private cloud deployment for full control over data location
  
  

These capabilities ensure that your AI agent operates within the bounds of corporate policy and regulatory requirements. You can confidently deploy an assistant knowing it respects access controls and leaves an audit trail of its activities. For instance, RBAC rules can ensure the agent only retrieves HR data for HR team members, or that finance-related queries are visible only to finance staff. Every action the agent takes (like updating a record or sending an email) is recorded with a timestamp and details, so nothing happens without oversight.

On the observability front, Klart AI provides real-time dashboards to monitor usage and performance. IT and ops leaders get full visibility into how the agent is being used – number of queries, peak times, popular sources, and more. You can track impact metrics such as tickets deflected, hours saved, or average response time improvement. Even answer quality can be measured via user feedback or accuracy audits. These insights help you continuously improve the system and clearly demonstrate its value to stakeholders.

Pro-Tip: Treat your AI agent like any other mission-critical system – set up proper governance from day one. Define clear roles and permissions for agent usage (for example, limit finance data access to finance team members) and enable logging/alerts for unusual activity. Early alignment with your security team will prevent issues and build confidence in the deployment across your organisation’s leadership.

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Reference Implementation (Klart AI Blueprint)

To illustrate how these layers come together, here’s a Klart AI Blueprint for implementing an agent:

1. Define the Use Case – Outline what your agent will do (e.g. assist IT support or answer customer FAQs) and what success looks like (KPIs like ticket deflection rate or time saved).
   
   
2. Connect Knowledge Sources – Set up connectors for your chosen data (SharePoint, Confluence, databases, etc.). Once connected, the platform will index and embed this content into its vector store.
   
   
3. Create & Configure the Agent – In the agent console, create a new agent profile. Assign it the connected data sources from step 2 and choose an appropriate LLM for generation (with fallback models if needed). This defines the agent’s “brain” – what it knows and which AI model it uses.
   
   
4. Enable Action Blocks – Activate any needed actions (email, Slack, SQL queries, etc.) for this agent. Configure details for each action, like email templates or API endpoints. Map triggers or intents to these actions (e.g. the intent “reset password” invokes the password-reset webhook and email block).
   
   
5. Apply Security & Permissions – Use the governance layer to enforce access rules. Ensure the agent only retrieves sensitive data for authorised users, and require manager approval for certain high-impact actions if needed. Enable SSO for user authentication and log all agent activity for auditing.
   
   
6. Deploy & Monitor – Launch the agent to your users via their preferred channel (web chat, Slack, Teams, etc.). Track usage and performance on the dashboard. Gather feedback and refine the agent’s knowledge base or action logic in an iterative loop.
   
   

For instance, to allow your agent to safely query data in Snowflake, you might create a Snowflake External Function that calls the Klart AI API. This lets the agent execute SQL on Snowflake without direct database access. Here’s a pseudo-SQL snippet illustrating that integration:

-- Pseudo-SQL: Register an external function in Snowflake for the AI agent

CREATE EXTERNAL FUNCTION AI_ASK(query TEXT)

RETURNS VARCHAR

API_INTEGRATION = KLARTAI_INT

HEADERS = ('Authorization' = 'Bearer <token>')

AS 'https://api.klartai.com/ask';

This is a sample

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With such an integration, a natural language request can be turned into a secure SQL query via the external function. This is just one example of how the blueprint’s components combine to create a seamless question-to-action flow in an enterprise environment.

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Case Study – Automates 70 % of Support Tickets

A leading enterprise marketplace SaaS provider, faced a surge in support tickets – many of them repetitive “how-to” requests that consumed valuable agent time. They implemented a Klart AI virtual assistant on their support portal to offer instant, automated help. After training it on product documentation and past tickets, the AI agent now resolves about 70% of incoming support queries without human intervention. Customers receive immediate answers and solutions, dramatically reducing the support team’s workload. Human agents are freed up to focus on the remaining 30% of complex cases, resulting in faster response times and higher customer satisfaction. The AI assistant quickly became a true “game-changer” for support efficiency and scale.

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Build Checklist & ROI Calculator

Before you embark on building your own enterprise AI agent, make sure you have all the prerequisites in place. Here’s a quick build checklist to set your project up for success:

• Executive Sponsor & Use Case – Secure buy-in from leadership and choose a clear initial use case with measurable impact (e.g. IT helpdesk self-service, sales assistant, etc.).
  
  
• Data Inventory – Gather the key knowledge assets your agent will need (internal docs, manuals, databases) and ensure they’re accessible for integration.
  
  
• Integration Access – Prepare API keys/credentials for each system you plan to connect (CRM, knowledge base, email server, Slack workspace, etc.).
  
  
• Security Review – Involve your security/compliance team early to review requirements (SSO setup, data encryption, user permissions) and address any regulatory needs (e.g. GDPR).
  
  
• Success Metrics – Define what success looks like (e.g. % of tickets automated, hours saved per week, reduction in response time) to track ROI.
  
  
• Pilot Team & Feedback – Select an initial group of users or a department to pilot the assistant. Provide a feedback channel for them to report issues and suggestions.
  
  
• Training & Change Management – Inform your users about the new AI assistant and provide guidance on how to interact with it. Manage expectations and highlight that it’s a tool to assist them, not a replacement.
  
  
• ROI Analysis – Estimate the potential return on investment using concrete numbers. Use our ROI Calculator (Excel download) to model your costs and savings based on factors like agent count, query volume, and automation rate.
  
  

With a solid plan and the right preparation, you’ll be ready to build and deploy an enterprise AI agent that can chat and act at scale. Each layer of the architecture – from data connectors to governance – will contribute to a system that is powerful, secure, and cost-effective.

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