diff --git a/01-Part_One/Chapter_6-Planning-18vvNESEwHnVUREzIipuaDNCnNAREGqEfy9MQYC9wb4o.md b/01-Part_One/Chapter_6-Planning-18vvNESEwHnVUREzIipuaDNCnNAREGqEfy9MQYC9wb4o.md
index ece3534..f1d77d5 100644
--- a/01-Part_One/Chapter_6-Planning-18vvNESEwHnVUREzIipuaDNCnNAREGqEfy9MQYC9wb4o.md
+++ b/01-Part_One/Chapter_6-Planning-18vvNESEwHnVUREzIipuaDNCnNAREGqEfy9MQYC9wb4o.md
@@ -105,7 +105,7 @@ Fig. 1: Google Deep Research agent generating an execution plan for using Google
 
 A key architectural component is the system's ability to manage this process asynchronously. This design ensures that the investigation, which can involve analyzing hundreds of sources, is resilient to single-point failures and allows the user to disengage and be notified upon completion. The system can also integrate user-provided documents, combining information from private sources with its web-based research. The final output is not merely a concatenated list of findings but a structured, multi-page report. During the synthesis phase, the model performs a critical evaluation of the collected information, identifying major themes and organizing the content into a coherent narrative with logical sections. The report is designed to be interactive, often including features like an audio overview, charts, and links to the original cited sources, allowing for verification and further exploration by the user. In addition to the synthesized results, the model explicitly returns the full list of sources it searched and consulted (see Fig.2). These are presented as citations, providing complete transparency and direct access to the primary information. This entire process transforms a simple query into a comprehensive, synthesized body of knowledge.
 
-![An example of Deep Research plan being executed, resulting in Google Search being used as a tool to search various web sources](../assests/Example_of_Deep_Research_Plan_Being_Executed_Resulting_in_Google_Search_being_used_as_a_Tool_to_Search_Various_Web_Sources.png)
+![An example of Deep Research plan being executed, resulting in Google Search being used as a tool to search various web sources](../assets/Example_of_Deep_Research_Plan_Being_Executed_Resulting_in_Google_Search_being_used_as_a_Tool_to_Search_Various_Web_Sources.png)
 
 Fig. 2: An example of Deep Research plan being executed, resulting in Google Search being used as a tool to search various web sources.
 
diff --git a/01-Part_One/Chapter_7-Multi-Agent_Collaboration-1RZ5-2fykDQKOBx01pwfKkDe0GCs5ydca7xW9Q4wqS_M.md b/01-Part_One/Chapter_7-Multi-Agent_Collaboration-1RZ5-2fykDQKOBx01pwfKkDe0GCs5ydca7xW9Q4wqS_M.md
index eab9474..7da01ca 100644
--- a/01-Part_One/Chapter_7-Multi-Agent_Collaboration-1RZ5-2fykDQKOBx01pwfKkDe0GCs5ydca7xW9Q4wqS_M.md
+++ b/01-Part_One/Chapter_7-Multi-Agent_Collaboration-1RZ5-2fykDQKOBx01pwfKkDe0GCs5ydca7xW9Q4wqS_M.md
@@ -66,7 +66,7 @@ This model is a nuanced extension of the "Supervisor" concept, where the supervi
 
 The "Hierarchical" model expands upon the supervisor concept to create a multi-layered organizational structure. This involves multiple levels of supervisors, with higher-level supervisors overseeing lower-level ones, and ultimately, a collection of operational agents at the lowest tier. This structure is well-suited for complex problems that can be decomposed into sub-problems, each managed by a specific layer of the hierarchy. It provides a structured approach to scalability and complexity management, allowing for distributed decision-making within defined boundaries.
 
-![Agents Communicate and Interact in Various Ways](../assests/Agents_Communicate_and_Interact_in_Various_Ways.png)
+![Agents Communicate and Interact in Various Ways](../assets/Agents_Communicate_and_Interact_in_Various_Ways.png)
 
 Fig. 2: Agents communicate and interact in various ways.