# Overview

GLIK offers four foundational app types, each representing a distinct execution model for building and deploying intelligent systems. App types define how user input is handled, how state is managed, and which blocks and system features are available.

This guide helps teams understand when to use each type, based on enterprise scenarios such as internal approvals, client-facing automation, agent workflows, or memory-aware assistants.

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### 🧩 GLIK App Types at a Glance

| App Type          | Execution Model                  | Ideal For                                       |
| ----------------- | -------------------------------- | ----------------------------------------------- |
| **Chatbot**       | Stateless, prompt-response logic | Basic Q\&A bots, static form handlers           |
| **Agent**         | Stateful with scoped memory      | Policy engines, multi-step enterprise flows     |
| **Advanced Chat** | Threaded, turn-based interaction | Assistants, advisors, conversation memory       |
| **Workflow**      | Graph-based logic with branching | Conditional processes, escalation, integrations |

### Use Case Breakdown & Guidance

#### **1. Chatbot**

**Model:** Stateless input/output\
**Best For:**

* Simple support bots (FAQs, policy links)
* Front-line triage in internal tools
* Single-turn verification bots (e.g., invoice status check)

**Limitations:**

* No persistent state or scoped memory
* Cannot use advanced memory or fallback blocks
* Not suitable for multi-step processes or regulated logic flows

***

#### **2. Agent**

**Model:** Stateful with memory and policy enforcement logic\
**Best For:**

* Expense approval agents
* HR onboarding logic
* Compliance evaluations
* Vendor risk screening

**Features:**

* Scoped memory (`invoice_data`, `policy_memory`, etc.)
* Full access to Variable Assigners, IF/ELSE, Tool Nodes
* End-to-end workflow continuity

**Considerations:**

* Does not use threaded chat interface
* LLM blocks must be explicitly invoked as steps
* Memory must be manually written/read using memory blocks

***

#### **3. Advanced Chat**

**Model:** Conversational memory and multi-turn assistant\
**Best For:**

* Executive assistants
* Internal helpdesk bots
* SOP navigators
* Contract review advisors

**Features:**

* Supports `conversation_variables` natively
* Allows fallback memory injection into prompts
* Multi-turn interaction flows without defining graph logic

**Caveats:**

* Cannot chain logic using IF/ELSE or Tool blocks
* Not ideal for deterministic policy enforcement
* Suited for reasoning and conversational exploration, not decision routing

***

#### **4. Workflow**

**Model:** Visual flow with branching logic and tool integrations\
**Best For:**

* Escalation frameworks (e.g., “if fails → reroute”)
* Multi-agent orchestration
* Cross-system automation (API → plugin → memory)
* Human-in-the-loop decision gates

**Features:**

* Full block access: LLM, Tool, Data Enrichment, Save Point, Knowledge
* Conditional logic supported at multiple branches
* Integrates with external APIs and internal memory stores

**Caveats:**

* Requires upfront logic design
* Best suited for technical users or implementation partners
* Works best with deterministic or well-scoped problem domains

***

### Memory & Variable Support Summary

| Feature                      | Chatbot | Agent | Advanced Chat | Workflow |
| ---------------------------- | ------- | ----- | ------------- | -------- |
| Conversation Variables       | ✖       | ✖     | ✅             | ✖        |
| Scoped Memory (e.g. session) | ✖       | ✅     | ✅             | ✅        |
| Tool Node / Plugin Access    | ✖       | ✅     | ✖             | ✅        |
| Conditional Routing          | ✖       | ✅     | ✖             | ✅        |
| Visual Flow Design           | ✖       | ✖     | ✖             | ✅        |

### Choosing the Right App Type

| Scenario                                          | Recommended App Type |
| ------------------------------------------------- | -------------------- |
| Automating policy approval workflows              | Agent or Workflow    |
| Building an internal compliance chatbot           | Advanced Chat        |
| Creating a no-memory Q\&A interface               | Chatbot              |
| Integrating multiple decision layers with routing | Workflow             |
| Designing a memory-enabled SOP navigator          | Advanced Chat        |
| Embedding policy logic into a partner SaaS tool   | Agent                |


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