Overview
AI Agent for Sentiment & Tone Analysis - Decode sentiment for faster, smarter customer support. Test the AI agent that analyzes customer communications to help teams respond quickly and with care. What you'll experience Instant Communication Analysis Watch the agent process messages to detect emotional cues and urgency in real time. Sentiment and Tone Insights See AI evaluate sentiment (positive/neutral/negative) and tone (urgency, formality, emotion) with clear justifications. Smarter Response Prioritization See how the agent helps your team identify urgent messages and improve support efficiency. Powered by AWS Runs on trusted cloud infrastructure with built-in AI tools like Amazon Bedrock and SageMaker, delivering enterprise-grade performance and rapid results.
Highlights
- Instant Communication Analysis - Watch the agent process messages to detect emotional cues and urgency in real time.
- Sentiment and Tone Insights - See AI evaluate sentiment (positive/neutral/negative) and tone (urgency, formality, emotion) with clear justifications.
- Smarter Response Prioritization - See how the agent helps your team identify urgent messages and improve support efficiency.
Details
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Delivery details
- Amazon Bedrock AgentCore
API-Based Agents & Tools
API-Based Agents and Tools integrate through standard web protocols. Your applications can make API calls to access agent capabilities and receive responses.
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Usage instructions
API
Instructions
- You will need Postman installed on your system
- Download the "AWS_API_Based_Support_Case_Sentiment_Analysis_AIAgent.json” API Collection 3. Import the .json file into Postman as an API Collection 4. You will see 4 API Calls
Using the APIs
A. Authentication: Update the Body with the provided username and password and hit SEND. Once the call is successful it will give you a 200 OK response and generate a TOKEN.
B. Deploy: Select the Deploy Bot API and update the HEADER section. Replace the X-Authorization value with the recently provided TOKEN.
C. Deploy - Input Parameters: Update the Body to pass the input parameters. This AI Agent accepts 2 variables (Both these should be passed in the form of text):
i) Text to Analyze: A text content form the support ticket to analyze the customer's sentiment.
D. Deploy – Execution: Once the API is executed the response will be 200 OK and will deliver a DeploymentID and the AutomationName.
E. Get Activity: This API will show you the progress and current state of the Agent. When you have long-running processes, this API will allow you to capture the completion %. You must update the HEADER with the TOKEN and pass the DEPLOYMENTID in the Body of the API.
F. Get Job Execution Details: This API will fetch the API response from the AI Agent. You need to pass the TOKEN and the JOBID parameters in the API Call. A string containing the full analysis and response will be provided as part of the API response.
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If you have any questions or face any issues, please reach out to our support team at solutiontestdrive@automationanywhere.com
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Customer reviews
Automation has improved banking compliance checks but still needs richer integration options
What is our primary use case?
The primary use cases predominantly involve banking, including check validation, fraud management, and anti-money laundering. We extract these from the core banking system and pull them into the agent, which then provides a response that we return to the system.
I predominantly try to integrate Automation Anywhere AI Agent with ServiceNow tools and Dynatrace for data tracing. However, the data is not moving correctly onto the integration platform.
What is most valuable?
Assessing the impact of Automation Anywhere AI Agent on workflow efficiency depends on the type of use case we have, such as compliance-related matters. With agentic AI, we achieve zero error. Otherwise, we calculate the cost and return on investment for that specific area. We also check the accuracy of our output and whether hallucination is present.
The data extraction feature has helped in processing tasks, but we use our own ingestion model rather than the Automation Anywhere model. We go with Hadoop and a data lake approach, process it internally, and then push the regular data to Automation Anywhere AI Agent.
The integration capabilities ensure smooth operations. We observe what comes into the pipeline and see the differences. I feel Automation Anywhere AI Agent is better than Blue Prism in that specific context. Generally, we go for Blue Prism in banking use cases, but of late, we are moving to Automation Anywhere AI Agent.
What needs improvement?
When considering weaknesses and improvements, the platform does not give us the liberty to use our own features where we can bring out creativity. We have to map the process to whatever is available, and the bidirectional integration is missing. If we have another agent coming from another tool and we are using it, integration is very difficult.
Price is an issue because when we go with AI, we are getting more efficiency. Comparatively to other tools, it is on the higher side. However, we do not consider the price point when we choose this kind of tool for banking. We go for the compliance side, license feasibility, and other factors. Pricing is high in the market, but we are not in pricing mode. We see other advantages where we can use license solutions that can be used in the majority of cases. We give business-as-usual support to the customer, so we bring in the cost to our bandwidth and do not charge the customer. There are many combinations we employ.
Additional features I would like to see in the future include faster learning capabilities. For single use cases, we require multiple agentic AIs. If we could combine them into one and do the decision-making with a single price, that would be better. Currently, for each specific condition, we use one agentic AI and then combine another agentic AI on top of it that controls all the conditions. This creates a complex situation, but if it were simplified, it would be good.
For how long have I used the solution?
I have been working with Automation Anywhere AI Agent for around seven months.
Which solution did I use previously and why did I switch?
If Automation Anywhere AI Agent does not fit the requirement, we make our own tools. We do not generally do this because the complexity is now greater. We are in the banking area, so we cannot move the data outside the premises. If it does not meet our needs and Blue Prism is not nearby, we make our own features and then provide a Gen AI solution for that if Automation Anywhere AI Agent does not come into the picture. In that case, we have an additional margin. Our competency is only Automation Anywhere AI Agent; we do not have Blue Prism competency in RPA . The companies I have worked for are only using Automation Anywhere AI Agent after initially using Blue Prism. We use only one tool at a time.
How was the initial setup?
The initial setup for Automation Anywhere AI Agent is straightforward. We take the low-hanging fruit so it comes very clearly, and then we get approval from all stakeholders and institutionalize it. The initial setup is good, but expansion or scaling becomes a bit difficult or complex. Still, compared to other tools, it is good.
What other advice do I have?
I do not use the Process Analytics feature.
I would describe the role of Cognitive Automation skills in managing complex tasks as very good. However, we are not using it now. They are moving more toward Gen AI, and Cognizant is not using much of Cognitive Automation skills anymore.
I am also dealing with BotFarm by Automation Anywhere AI Agent.
I would recommend BotFarm for companies in banking, financials, and wealth management, so all in the BFSI sector. Whether small bank or big bank, that is what we do.
BotFarm is not very popular; the standard Automation Anywhere AI Agent is more widespread.
I feel that Automation Anywhere AI Agent is the best option on the market when it comes to automation. I would rate this review a 7 out of 10.