updated a link

_posts/2024-04-19-Data-Agents-With-LalaIndex.md

@@ -18,7 +18,7 @@ The main core steps of a Data Agent include:
 - **Reasoning Loop:** This loop determines how the Agent will interact with different data tools. LlamaIndex supports a [variety of Agents and functionalities](https://docs.llamaindex.ai/en/stable/examples/agent/Chatbot_SEC/), including:
    - [**ReAct Agent:**](https://docs.llamaindex.ai/en/stable/examples/Agent/react_Agent_with_query_engine/) It involves a 3-step process: Thought (determine which tool to use), Action (use the tool to take an action), and Observation (check the result and iterate until a final result is obtained).
    - [**Function Calling Agent:**](https://docs.llamaindex.ai/en/stable/examples/Agent/openai_Agent_parallel_function_calling/) A unified abstraction that utilizes function calling capabilities of different LLMs to call given tools.
-   - [**Chain-of-Abstraction Agent:**][https://docs.llamaindex.ai/en/stable/examples/agent/coa_agent/] Implements a generalized version of the strategy described in the [original paper](https://arxiv.org/pdf/2401.17464.pdf). It enables LLMs to learn more general reasoning strategies that are robust to shifts of domain knowledge relevant to different reasoning questions.
+   - [**Chain-of-Abstraction Agent:**](https://docs.llamaindex.ai/en/stable/examples/agent/coa_agent/) Implements a generalized version of the strategy described in the [original paper](https://arxiv.org/pdf/2401.17464.pdf). It enables LLMs to learn more general reasoning strategies that are robust to shifts of domain knowledge relevant to different reasoning questions.
    - [**Retrieval Augmented:**](https://docs.llamaindex.ai/en/stable/examples/Agent/openai_Agent_retrieval/) Utilizes an Agent together with a tool retriever to manage an index on an arbitrary number of data tools, reducing latency and cost by retrieving only relevant tools.
    - [**Controlling Reasoning Loop:**](https://docs.llamaindex.ai/en/stable/examples/Agent/return_direct_Agent/?h=return_direct) Allows modification of the Agent reasoning loop, where the output is returned directly instead of using an LLM, useful for speeding up response times.
    - [**Step-wise Controllable:**](https://docs.llamaindex.ai/en/stable/examples/agent/agent_runner/agent_runner/) Provides more granular control of the Agent by separating task creation and execution, enabling sharing feedback to the Agent as it completes tasks.