fix(docs): change illustrations in concepts (#946)

pages/concepts/agent-services/agent-hosting.mdx

@@ -15,7 +15,7 @@ With the world moving every increasingly towards an automated future where techn
 Deploying a new agent is quick and easy. You can have a new running agent deployed in a matter of a few clicks, and with many pre-made examples you can have an active agents doing tasks for you with ease. You can either choose to build an agent from a blank script, by clicking on **+ Agents**, or you can choose to create your agent based on a specified template, by clicking on **+ Use Case**.
 
     <Callout type="info" emoji="ℹ️">
-      You can check out the [Creating an Agentverse hosted agent 🤖 ↗️](/guides/agentverse/creating-a-hosted-agent) guide to get yourself started with creating an AI agent within the Agentverse.
+      You can check out the [Creating an Agentverse hosted agent ↗️](/guides/agentverse/creating-a-hosted-agent) guide to get yourself started with creating an AI agent within the Agentverse.
     </Callout>
 
 The **My Agents** section manages all the important steps about making sure that your agent stays online. With a targeted 100% uptime, your agent will not sleep unless you tell it to. By it being a hosted agent on the Agentverse, the agent will always be kept up-to-date on the [Almanac ↗️](/references/contracts/uagents-almanac/almanac-overview) contract. This latter one also manages your agent private keys for you too, keeping these safe and secure.

pages/concepts/agent-services/agent-mail.mdx

@@ -14,4 +14,4 @@ The mailroom service also enables communication between agents registered in Age
 
 ## Next steps
 
-Have a look at our [Agentverse guides ↗️](/guides#agentverse) and in particular at the [Utilizing the Agentverse Mailroom service 📬 ↗️](/guides/agentverse/utilising-the-mailbox) guide for a better understanding of the registration process and to see how local and Agentverse agents communicate with one another using this tool.
+Have a look at our [Agentverse guides ↗️](/guides#agentverse) and in particular at the [Utilizing the Agentverse Mailroom service ↗️](/guides/agentverse/utilising-the-mailbox) guide for a better understanding of the registration process and to see how local and Agentverse agents communicate with one another using this tool.

pages/concepts/ai-engine/ai-engine-intro.mdx

@@ -7,7 +7,7 @@ import PackageVersion from 'components/package-version'
 
 The **AI Engine** is a system that combines [Agents ↗️](/concepts/agents/agents) with human-readable text input to create a scalable AI infrastructure that supports Large Language Models (LLMs). It is at the heart of [DeltaV ↗️](/concepts/ai-engine/deltav) and its functionalities. The goal of the AI Engine is to analyse, understand and link human input to agents by facilitating natural language interactions. The AI Engine reads user input, converts it into actionable tasks and selects the most appropriate AI agent registered in the Agentverse to perform the Objective Tasks provided by users.
 
-![](../../../src/images/concepts/ai-engine/system_diagram_ai_engine.svg)
+![](../../../src/images/concepts/ai-engine/system_diagram_ai_engine.png)
 
 The AI Engine is characterised by a variety of different tasks. It is able to provide answers to complex queries and then carry out various actions, such as making a booking for a hotel. This is achieved through its ability to understand users' preferences and goals through **contextual understanding**. The AI Engine examines trends and turns random inputs into meaningful insights by evaluating previous interactions. When uncertainty comes into play, the AI Engine solicits feedback from the user to verify that its suggestions and recommendations match the user's end goal. In this way, the AI Engine actively anticipates the needs of users and adapts to them. We look forward to creating an ecosystem in which technology becomes an ally in achieving users' goals.
 

pages/concepts/ai-engine/deltav.mdx

@@ -8,9 +8,11 @@ DeltaV works as an AI-based chat interface. DeltaV acts as a front-end interface
 
 DeltaV has been developed with the intent to connect users to Agents. DeltaV is the portal to the [AI Engine ↗️](/concepts/ai-engine/ai-engine-intro), [Agents ↗️](/concepts/agents/agents), and the [Agentverse ↗️](/concepts/agent-services/agentverse-intro) platform.
 
-Developers can employ Fetch.ai Agents technology and encapsulate **Large Language Models (LLMs)**, **Machine Learning (ML)** models, **existing APIs**, and other **business logic** to make services accessible via DeltaV.
+Developers can employ Fetch.ai Agents technology and encapsulate **Large Language Models (LLMs)**, **Machine Learning (ML)** models, **existing APIs**, and other **business logic** to make [Agent Functions ↗️](/guides/agents/intermediate/agent-functions) accessible via DeltaV.
 
-Start developing your Agents to encapsulate Agent Functions and register your agents within the **Agentverse** to make such Functions retrievable on DeltaV. Checkout the dedicated resources for a better understanding of Agent Functions and registration process on the Agentverse: [Agentverse Functions ↗️](/guides/services/services) and [registering agents as Agent Functions on the Agentverse ↗️](/guides/agentverse/registering-agent-services). Also, checkout this dedicated [guide ↗️](/guides/agents/running-locally) in case you are developing your agents locally.
+Start developing your Agents to encapsulate Agent Functions and register your agents within the **Agentverse** to make such Functions retrievable on DeltaV.
+
+Checkout the dedicated resources for a better understanding of Agent Functions and registration process on the Agentverse: [Agentverse Functions ↗️](/guides/services/services) and [registering agents as Agent Functions on the Agentverse ↗️](/guides/agentverse/registering-agent-services). Also, checkout this dedicated [guide ↗️](/guides/agents/running-locally) in case you are developing your agents locally.
 
     <Callout type="info" emoji="ℹ️">
       Head to [DeltaV ↗️](https://deltav.agentverse.ai/), sign in and get started with your first request!

pages/concepts/ai-engine/powering-connections-and-smart-operations-in-deltav.mdx

@@ -1,8 +1,8 @@
 # Powering connections and smart operations in DeltaV
 
-The AI Engine stands at the core of [DeltaV ↗️](https://deltav.agentverse.ai/login) and its features, as it allows users and developers to connect to a wide range of agent-based services. Once an agent is [registered ↗️](/guides/agentverse/registering-agent-services), the offered service is visible to the AI Engine and it can start connecting users and services.
+The AI Engine stands at the core of [DeltaV ↗️](https://deltav.agentverse.ai/login) and its features, as it allows users and developers to connect to a wide range of agent-based [functions ↗️](/guides/agents/intermediate/agent-functions). Once an agent is [registered ↗️](/guides/agentverse/registering-agent-services), the offered Agent Function is visible to the AI Engine and it can start connecting users and Agent functionalities.
 
-![](../../../src/images/concepts/ai-engine/ai_with_personal_data.svg)
+![](../../../src/images/concepts/ai-engine/ai_with_personal_data.png)
 
 This system is equipped with personalized capabilities, supported by an internal agent that performs tasks efficiently. An internal agent is created by the AI Engine and made available for communication via the DeltaV user interface. The AI Engine interprets the human text input provided to the agent and starts working asynchronously on your behalf as soon as it receives your intent. This customized method uses Large Language Models (LLMs), which are essential for improving the AI Engine's understanding, coordination and problem-solving capabilities.
 
@@ -24,12 +24,12 @@ In this context, a **Primary function** refers to an agent function that provide
 
 ## Deconstructing tasks: context building and smart routing
 
-![](../../../src/images/concepts/ai-engine/human_text_in.svg)
+![](../../../src/images/concepts/ai-engine/human_text_in.png)
 
 Finding new information is a key focus of the AI Engine to significantly improve the user journey. This is crucial for the execution of Agent Functions, such as booking a hotel room for your holiday in a specific city. In an environment where reservations are centralized, this seems like a simple process. However, for the booking to be successful, the AI Engine must be able to understand the user's input and objectives and communicate with multiple agents. At this stage, the AI Engine's ability to **understand and plan** is very important: the user's goal is broken down into a series of smaller primary and secondary functions, each representing an integral step towards the desired end result. This coordination may be automatic, or in certain situations where the AI Engine is unsure, it may require user input to confirm the function selection.
 
 **Context building** plays a crucial role, allowing the AI Engine to continuously improve its understanding by transforming data. Context building is an ongoing process within the AI Engine that involves the continuous improvement of the knowledge base during the AI Engine session. In other words, context building is the continuous act of collecting and/or transforming new knowledge to complete a task.
 
-![](../../../src/images/concepts/ai-engine/hotel_tasks_go_through_the_engine_to_representative_agents.svg)
+![](../../../src/images/concepts/ai-engine/hotel_tasks_go_through_the_engine_to_representative_agents.png)
 
 The final step of the AI Engine is **smart routing**, that is the ongoing process within the AI Engine that makes it aware of all registered Agents and Agent Function according to the objective for which they are best suited for. This process takes into account the context and past performance history of these agents to guide the AI Engine's decision-making process. In this way, the AI Engine selects the most suitable agents, taking into account the agents' functions and their past performance metrics. **Trust** becomes a key factor, favoring agents with a track record of reliable behavior. Smart routing not only ensures the completion of objectives, but also creates a sense of reliability and efficiency in the operations.

pages/concepts/introducing-fetchai.mdx

@@ -12,9 +12,9 @@ These services make up the core architecture of Fetch.ai
 
 **Agents**, **Agentverse**, **AI Engine**, and **Fetch network**.
 
-Agents register to Alamanac so that they can be discovered in the network by other agents. Agents may register their functions to Agentverse so that AI-Engine can index these agents so that they can be called by AI-Engine. The Fetch network offers a layer of truth and trust by inherently being open.
+Agents register to Almanac so that they can be discovered in the network by other agents. Agents may register their functions to Agentverse so that AI-Engine can index these agents so that they can be called by AI-Engine. The Fetch network offers a layer of truth and trust by inherently being open.
 
-![](../../src/images/concepts/about/High-level-system-diagram.svg)
+![](../../src/images/concepts/about/High-level-system-diagram.png)
 
 ### Agents: the core
 
@@ -25,16 +25,16 @@ Agents can come together to become multi-agent workflows, single agents which ca
 
 Agents are built with the  **uAgents Framework**, a lightweight library designed to facilitate the development of decentralized **Agents**. At the uAgents core is an open sourced communication protocol for agents.
 
-![](src/images/concepts/ai-agents/Agents_interacting.svg)
+![](src/images/concepts/ai-agents/Agents_interacting.png)
 
 
 Agents can fundamentally change the way we see complicated systems. For example, supply chain management could deploy Agents using the uAgents Framework to improve operations at various stages. Demand forecasting, inventory control, logistics optimization, supplier relationships monitoring, quality control and risk mitigation in all areas can be done with their help. Agents could transform supply chain operations by increasing efficiency, reducing costs, improving accuracy and providing real-time visibility.
 
 These agents are the building blocks that allow developers to gain access to the tools and resources provided by the uAgents Framework, enabling them to create and participate in intelligent and self-managed systems that can be used in various real-world domains.
 
-![](src/images/concepts/ai-agents/decentralised_network.svg)
+![](src/images/concepts/ai-agents/decentralised_network.png)
 
-Agents can wrap and cannabilise LLMs to create personalised agents for any task. With the rise of Large LanguageModels (LLMs) and AI-related products, autonomous intelligent agents have become the link between these models and tools. They are revolutionizing the way we solve problems, make decisions and collaborate with each other.
+Agents can wrap and cannibalise LLMs to create personalised agents for any task. With the rise of Large LanguageModels (LLMs) and AI-related products, autonomous intelligent agents have become the link between these models and tools. They are revolutionizing the way we solve problems, make decisions and collaborate with each other.
 
 The concept of agents refers to autonomous, decentralized microsystems that overcome conventional limitations. Agents provide a gateway to a future where intelligent agents alongside the Fetch network and the [AI Engine ↗️](/concepts/ai-engine/ai-engine-intro), can communicate, negotiate and collaborate to streamline complex tasks, solve complicated problems and improve decision-making processes in various fields.
 
@@ -52,7 +52,7 @@ You can post your agent(s) and their functions on Agentverse so that the AI Engi
 
 The **AI Engine** is at the heart of DeltaV's functionalities; the AI Engine's aim is to parse, comprehend, and link human input to agents by facilitating natural language interactions. The AI Engine reads user inputs, converts them into actionable objectives, and selects the most suitable Agent registered in the Agentverse for objective task execution.
 
-### The Fetch.ai network: the foundations
+### The Fetch.ai Network: the foundations
 
 The **Fetch.ai network** serves as the foundation of Fetch.ai's entire ecosystem, underpinning and empowering the functionalities of Agents, the Agentverse, DeltaV, and the AI Engine. This is the structural framework that enables the decentralized digital economy envisioned by Fetch.ai.
 

pages/guides/agents/getting-started/whats-an-agent.mdx

@@ -13,15 +13,15 @@ The **uAgents Framework** is a lightweight library designed to facilitate the de
       Current version of the uAgents package is <PackageVersion packageName="uagents" packageType="pypi" />.
     </Callout>
 
-![](src/images/concepts/ai-agents/Agents_interacting.svg)
+![](src/images/concepts/ai-agents/Agents_interacting.png)
 
 Agents are autonomous software program built using the uAgents framework and that can interact autonomously with other agents in a decentralized environment. These agents can operate in a decentralized manner, but their decentralization remains optional and dependent on individual preferences or needs.
 
 Intelligent agents can fundamentally change the way we see complicated systems. For example, supply chain management could deploy Agents using the uAgents Framework to improve operations at various stages. Demand forecasting, inventory control, logistics optimization, supplier relationships monitoring, quality control and risk mitigation in all areas can be done with their help. Agents could transform supply chain operations by increasing efficiency, reducing costs, improving accuracy and providing real-time visibility.
 
 These agents are the basic building blocks that allow developers to gain access to the tools and resources provided by the uAgents Framework, enabling them to create and participate in intelligent and self-managed systems that can be used in various real-world domains.
 
-![](src/images/concepts/ai-agents/decentralised_network.svg)
+![](src/images/concepts/ai-agents/decentralised_network.png)
 
 ## Why Agents
 

pages/guides/agents/intermediate/public-private-agents.mdx

@@ -6,7 +6,7 @@ import { Callout } from 'nextra/components'
 
 Transparency is a fundamental principle in decentralized finance (DeFi) and blockchain systems. Within the Fetch.ai network, **users have the ability to determine the amount of information they wish to publish**. This is thanks to the ability for users to create Agents as either **public** or **private**, based on their **introspectivity** and **protocol exposure** through the [Agentverse ↗️](/concepts/agent-services/agentverse-intro) platform.
 
-![](src/images/concepts/ai-agents/public_and_private_agents.svg)
+![](src/images/concepts/ai-agents/public_and_private_agents.png)
 
 This allows users to provide greater flexibility to Agents, creating a balance between transparency and privacy for every operation they perform on the network.
 
@@ -16,7 +16,7 @@ In contrast, a financial institution developing an AI agent for secure transacti
 
 ## Defining public and private agents
 
-![](src/images/concepts/ai-agents/privacy_spectrum.svg)
+![](src/images/concepts/ai-agents/privacy_spectrum.png)
 
 In this context, users may not be willing to share information but want to keep it private for some reason. This is possible thanks to the ability to distinguish between **public** and **private** actors.