AI Agents and Their Potential in Risk and Emergency Communication

Sponsored by

Dear reader,

In this edition of the Wag The Dog newsletter I will look at an exciting development that could drastically change how we manage crises and emergencies: AI Agents. 

I'll explore how these advanced systems are different from traditional AI and examine their potential applications in risk and emergency communication, with a spotlight on a cutting-edge AI agent named Agent Q.

And no, AgentQ is not related to 007 at all…😅

Enjoy and let me know what you think!

Table of Contents

• Understanding AI agents: Beyond traditional AI and …

• Introducing Agent Q: A pioneer in AI agent technol …

• Possible applications of AI agents in risk and eme …

  • 1. Real-time information sharing

    • Automated alert systems:

    • Multi-channel communication:

    • Language and accessibility customisation:

  • 2. Dynamic situation analysis and response coordin …

    • Centralised information hub:

    • Optimisation of resource allocation:

    • Coordination between authorities:

  • 3. Public communication and engagement

    • Social media monitoring and engagement:

    • Personalised risk communication:

    • Rumour checking and fact checking:

  • 4. Training and simulation

    • Realistic scenario creation:

    • Performance analysis and feedback:

    • Continuous learning:

  • 5. Predictive analyses and early warning systems

    • Early threat detection:

    • Risk assessment and mapping:

    • Scenario modelling:

• Challenges and considerations

• The future of emergency communication?

• References and further reading.

• Sponsor

• What I am reading/testing/checking out:

• Let’s meet

Understanding AI agents: Beyond traditional AI and chatbots

Before we get into the details, let's clarify what we mean by AI agents and how they are different from the AI systems you may be familiar with, such as chatbots or generative AI models.

AI agents are sophisticated software units designed to act autonomously in complex, dynamic environments. Unlike conventional AI systems or chatbots, which usually respond to specific inputs or commands, AI agents can:

• Plan and execute multi-step tasks: They can break down complex problems into manageable steps and execute them in sequence.

• Adapt to changing conditions: AI agents can change their behaviour based on new information or unexpected changes in their environment.

• Learn from experience: Through techniques such as reinforcement learning, these agents can improve their performance over time.

• Interact with their environment: They can gather information, make decisions and take actions that affect the world around them.

• Collaborate with humans and other systems: AI agents can collaborate with human operators and integrate with existing technologies to enhance their overall capabilities.

Check out this video from my ex-colleagues at IBM if you want to learn more.

This level of autonomy and adaptability sets AI agents apart from static AI systems and makes them particularly valuable in the unpredictable world of emergency management.

Introducing Agent Q: A pioneer in AI agent technology

To illustrate the potential of AI agents in our field, let's look at a concrete example: Agent Q, developed by MultiOn¹ .

I only recently discovered the company and got the idea for this newsletter issue while reading the documentation.

Agent Q represents a significant advance in AI agent technology. It combines several advanced techniques to create a system that can handle complex, multi-step tasks in dynamic environments.

The key features of Agent Q are:

Guided search with Monte Carlo Tree Search (MCTS):

✅ This allows Agent Q to explore different actions and scenarios autonomously, balancing between exploring new options and utilising known successful strategies.

AI self-criticism:

✅ At each decision step, Agent Q exercises self-criticism to refine its decisions. This is particularly useful for long-term planning, where there can be little immediate feedback.

Direct Preference Optimisation (DPO):

✅ This learning algorithm allows Agent Q to improve its performance by learning from both successful and sub-optimal outcomes.

These capabilities make Agent Q a good example of an AI agent suited to emergency management tasks where adaptability, foresight and continuous improvement are critical.

Check out their demo video (mainly practical office and travel applications but still…).

Possible applications of AI agents in risk and emergency communication

Now that we know what AI agents are and have Agent Q as an example, let's explore how these technologies could transform risk and emergency communications.

It's important to note that while these applications are promising, they are still largely theoretical.

The actual capabilities of AI agents like Agent Q in real emergency scenarios have yet to be fully tested and validated. With this in mind, here are some key areas where AI agents could make an important contribution:

1. Real-time information sharing

AI agents could completely change the way we disseminate important information in emergencies:

Automated alert systems:

👉 An AI agent could analyse incoming data from various sources (weather stations, earthquake monitors, traffic cameras, etc.) and create tailored alerts for different stakeholders.

For example, during an approaching hurricane, the agent could send evacuation instructions to residents in at-risk areas, deployment instructions to rescue teams and status reports to government officials - all simultaneously and in real time.

Multi-channel communication:

👉 AI agents could be able to manage communication across multiple platforms (SMS, email, social media, emergency call systems), ensuring that important information reaches the widest possible audience via their preferred channels.

Language and accessibility customisation:

👉 These agents could automatically translate alerts into multiple languages and adapt the content for people with different accessibility needs, which could help ensure inclusive communication in times of crisis.

2. Dynamic situation analysis and response coordination

AI agents could process large amounts of data and adapt to changing circumstances:

Centralised information hub:

👉 An AI agent could serve as a central point for collecting, analysing and distributing information from different agencies and sources. This would give all parties involved a standardised, real-time overview of the emergency situation, enabling better coordination and decision-making.

Optimisation of resource allocation:

👉 By continuously analysing the developing situation, an AI agent can predict resource requirements and suggest optimal distribution strategies. In the case of a forest fire, for example, it could direct firefighting resources to the most critical areas based on predictions of fire spread, population density and available resources.

Coordination between authorities:

👉 AI agents could facilitate seamless communication between different agencies by translating between different protocols and systems to ensure everyone is on the same page. This could reduce response times and improve the efficiency of multi-agency operations.

3. Public communication and engagement

Effective communication with the public is crucial in emergencies. AI agents could improve this in a number of ways:

Social media monitoring and engagement:

👉 AI agents could be able to monitor social media platforms in real time, identify emerging concerns, track the spread of misinformation and provide accurate updates. They could also engage directly with the public, answering questions and providing advice.

Personalised risk communication:

👉 By analysing demographic data and individual behaviour patterns, AI agents could tailor risk communication to specific target groups. For example, they could send tips on preparing for flooding to residents in low-lying areas or air quality warnings to people with respiratory diseases.

Rumour checking and fact checking:

👉 During an infodemic² , AI agents could quickly identify and counteract misinformation by providing verified facts and directing people to reliable sources of information.

4. Training and simulation

AI agents could significantly improve emergency preparedness training:

Realistic scenario creation:

👉 Agents like Q could be able to create highly detailed and dynamic training scenarios that adapt to the actions of participants. This would allow emergency responders to practise decision-making in complex, evolving situations that closely resemble real emergencies.

Performance analysis and feedback:

👉 After the training exercises, AI agents could analyse the participants' performance in detail, identify areas for improvement and suggest targeted training measures.

Continuous learning:

👉 By analysing data from real emergency operations and simulations, AI agents could be able to constantly update training scenarios to reflect the latest threats and best practises.

5. Predictive analyses and early warning systems

The potential ability of AI agents to process and analyse large amounts of data in real time could make them valuable for predictive analytics:

Early threat detection:

👉 By continuously monitoring various data sources (weather patterns, seismic activity, social media trends), AI agents could detect potential threats before they escalate into full-blown emergencies.

Risk assessment and mapping:

👉 AI agents could be able to create dynamic risk maps that are updated in real time based on changing conditions, helping emergency managers make informed decisions about resource allocation and public safety measures.

Scenario modelling:

👉 In the early stages of an emergency, AI agents could quickly model different scenarios and their potential consequences, helping decision makers choose the most effective response strategies.

Challenges and considerations

While the potential of AI agents for emergency management is exciting, it's important to approach their implementation with caution. Here are some important considerations:

1. Privacy and security: ensure that AI systems that handle sensitive information meet the highest privacy standards.

2. Human oversight: While AI agents can greatly enhance our capabilities, human judgement and decision-making are still essential, especially in high-stakes situations.

3. Ethical considerations: Develop clear guidelines for the use of AI in emergency situations that address issues such as fairness, transparency and accountability.

4. Integration with existing systems: Plan for seamless integration of AI agents into current emergency management tools and protocols.

5. Training and induction: Invest in training programmes to ensure that emergency management professionals are able to work with AI agents.

The future of emergency communication?

AI agents are a powerful new tool in our emergency management toolkit. By using their ability to process vast amounts of data, adapt to changing conditions and coordinate complex operations, we could improve our ability to prepare for, respond to and recover from emergencies.

I feel that it's important that professionals in emergency communication keep abreast of these developments and actively shape how AI agents are used in our field.

In this way, we can ensure that these technologies are used responsibly and effectively to achieve our ultimate goal: protecting lives and minimising the impact of emergencies on our communities.

What do you think?

PS: I’ll be testing out an AI agent platform soon and will keep you in the loop of my findings.

References and further reading.

1  Agent Q: Breakthrough AI Research in Self-Healing Web Agents | MultiOn — MultiOn AI. (2024, August 13). MultiOn AI. https://www.multion.ai/blog/introducing-agent-q-research-breakthrough-for-the-next-generation-of-ai-agents-with-planning-and-self-healing-capabilities

2  Wikipedia Contributors. (2024, June 24). Infodemic. Wikipedia; Wikimedia Foundation. https://en.wikipedia.org/wiki/Infodemic

Sponsor

Keep up with AI

How do you keep up with the insane pace of AI? Join The Rundown — the world’s largest AI newsletter that keeps you up-to-date with everything happening in AI with just a 5-minute read per day.

Sign up with one click.

What I am reading/testing/checking out:

• Article: What have we learned from the CrowdStrike crisis? Probably not much by Tony Jaques. Great follow up on my own article published previously.

• Tool: a no code AI agent building platform called Relevance AI

• Perplexity (get $10 off with this link): Ask your questions and receive concise, accurate answers backed up by a curated set of sources. It has a conversational interface, contextual awareness, and learns about your interests and preferences over time.

• Article: The World Is Not Ready for the Next Pandemic via Foreign Affairs

• Article: Auckland City Mission tracks food parcels, police investigate as meth found in donated lollies. Even an AI hallucination wouldn’t come up with such a scenario.

Let’s meet

Here are the events and conferences where I'll be speaking. If you're around, feel free to message me and we can meet up for a coffee.

• 🌍 International Public Relations Association (IPRA) Thought Leader Webinar: Using AI for Crisis Communications, Thursday 12 September, online.

• 🇧🇭 Al for Crisis Communications: Navigating Turbulent Times, 6-7 October, Manama, Bahrain

• 🇺🇸 Al and Crisis Communications: Navigating Turbulent Times, 10-11 October 2024, Chicago, USA

• 🇬🇧 Crisis Communications Boot Camp, 4-5 November, London, United Kingdom

• 🇺🇸 International Association of Emergency Managers (IAEM) Annual Conference, 7 November, Colorado Springs, USA (remote/virtual).

• 🇳🇿 Emergency Media and Public Affairs (EMPA) conference, 8 November, Wellington, New Zealand (remote/virtual)

PS: I hope you've enjoyed this newsletter! Creating it each weekend is a labour of love that I provide for free. If you've found my writing valuable, the best way to support it is by sharing it with others. Please click the share links below to spread the word with your friends and colleagues; it would mean so much to me. Thank you for reading!

Share on LinkedIn // Share on Facebook // Share on X (Twitter)

_{Parts of this newsletter were created using AI technology to draft content. In addition, all AI-generated images include a caption stating, 'This image was created using AI'. These changes were made in line with the transparency requirements of the EU AI law for AI-generated content. Some links in this newsletter may be affiliate links, meaning I earn a small commission if you click and make a purchase; however, I only promote tools and services that I have tested, use myself, or am convinced will make a positive difference.}