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Engineer

Beyond Automation
Agentic AI & the Intelligence Revolution

AI agents represent a fundamental shift in business capability:

  • While chatbots respond, agents act

  • While automation follows rules, agents understand context and adapt

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This isn't just faster automation. It's intelligent assistance at scale.

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Agent Intelligence: Your New Organisational Superpower
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Traditional AI offers processing power. Agent AI offers genuine intelligence at scale:

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  • Process information like humans, but at massive scale

  • Collaborate in teams to solve complex problems

  • Learn and adapt from experience

  • Operate within your existing organisational structures

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Think of agents not as tools, but as intelligent workers ready to amplify your team's capabilities.

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AI Agent Superpowers You're Missing

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Mass Personalized Interaction

  • Engage thousands of individuals simultaneously, each conversation uniquely tailored yet consistent with your brand

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Scalable Empathy

  • Provide emotionally intelligent interactions with unlimited patience - perfect for customer service, training, and support

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Generalist Expertise

  • Combine multiple specialist skills in single agents, streamlining workflows that traditionally required entire teams

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Iterative Intelligence

  • For the first time, software can blend intuitive understanding with rule-based precision, adapting approaches based on results.

 

Contextual Mastery

  • Process and synthesise vast amounts of information, making connections and insights impossible for human teams alone.

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Beyond Single Tasks: Intelligent Workflows
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AI agents excel individually, but their true power emerges in teams - collaborating with each other, your tools, and your people to handle entire business processes:

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Team Intelligence in Action

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  • Specialist agents work together like a virtual department

  • Each agent brings unique capabilities to the workflow

  • Teams adapt and self-correct as they work

  • Human oversight where you need it

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Tools & Integration

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  • Agents seamlessly use your existing software

  • Access databases, analyze spreadsheets, generate reports

  • Connect to CRM, ERP, and other business systems

  • Create end-to-end automated workflows

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For example: A team of agents can handle your entire RFP response process - researching requirements, finding relevant past proposals, drafting new content, and assembling the final document - all while keeping humans in the loop for strategic decisions.

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The Hidden Advantage: Organisational Intelligence
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Agent teams create a new form of organisational intelligence:

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  • Institutional knowledge becomes active and accessible

  • Expertise scales without traditional constraints

  • Complex workflows become fluid and adaptive

  • Organisations become more responsive and resilient

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Common Pitfalls to Avoid
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Most organisations stumble by

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  • Treating agents like traditional automation

  • Failing to leverage team dynamics

  • Missing opportunities for strategic transformation

  • Underestimating the need for data pipelines and agentic architecture

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Ready to explore how AI agents can transform your operations?

Let's discuss your objectives.

The Process

Slide 1

01
Identify Automation Opportunities

  • What are the goals, business model changes, staff expectations, technical goals?

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  • Our Approach: Discover business context from management. We interview staff (demand led) and make suggestions (supply led) for automation opportunities in the business

Studying in a library

Case Study
Market Research

Problem

The client wanted to automate a time consuming web research task. They would monthly search for competitor products, discovering the latest features and news, then boil that down into a spider graph comparing each product on common features. The benefit of AI agents is that this research be automated and updated on a monthly basis.

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Solution

We arranged large language models (LLM), like ChatGPT, into AI 'agents' to allow them to collaborate in a team. Each agent was be given a specific task within a workflow, just as a person would
 

The AI agent's workflow is similar to a person's for this task:

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  • Online web search for product information

  • If relevant information is found then the agent extracts it, else it continues the search until some limit is reached

  • Collate and summarise product information

  • Critique the summary versus sources for accuracy

  • Score the product against given criteria, or propose criteria

  • Present all products on one table or chart

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Recipe

The recipe for an agent is = LLM + Data + Tools + Environment

Recipe Continued...

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- LLM

We used Anthropic's Claude 3 Opus for reasoning, such as comparing products, and Anthropic's Sonnet for summarising long texts. Other models were made available, OpenAI GPT4 for reasoning and GPT3.5 for summarising.

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- Data

The data comes from web searches via a service called Tavily, which allows AI agents to search the web in general. We then custom built a bot to explore deeper within a discovered website for the product information.

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- Tools

- In addition to web searches, the AI team will have the ability to save their results in a tabular format, we chose excel and markdown.

- The team use a 'machine learning' tool called text embeddings to plot the products on a spider graph, comparing them on common features.

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- Environment

We grant the agent team access to the internet and a safe file location for saving their results.

Otherwise, all the team simply run on the client's local server , making calls to the LLM (Calude3) as required, as this is publicly available information.

Digital Programmer

Case Study
Data Science

Problem

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To fully automate the work of a data scientist with AI agents, expanding productivity of human data scientists by exploring options they had no time to consider. AI agents had to be adaptive to any situation, no step by step process could be prescribed. 

 

Solution

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This was a research project by Agentico for Microsoft Research and was so successful that is was built into their AutoGen project. Agentico built AI agents as data scientists, exploring data and algorithms given any objective. Not only are they highly adaptable, but human data scientists can easily inspect, repeat and adapt the agent's work in the same environment as the team were working. 

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Recipe

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The recipe for an agent is = LLM + Data + Tools + Environment

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- LLM

The AI is asked to take on three roles:

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  1. A data scientist / software coder

  2. A critic to review the coder's work

  3. A conversation manager, intelligently choosing which of the four participants should speak or act next

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Recipe Continued...

 

- LLM

A fourth role is played by straightforward code, not an LLM :

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4: The coding environment, safely executing code and returning results or errors for the team to review or correct.

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These roles were all played by OpenAI's GPT4, which remains the best at writing software. Role 3 is a foundational feature of Microsoft's Autogen, it is core to achieving solutions which have weakly described processes, or none at all.

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- Data

With a flexible objective, no single data source can be provided. Instead, the team are enabled to open a folder and find files of data via code.

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- Tools

- The team can execute code, this allows any tools which can be accessed via code, the internet or an API. 

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- Environment

The bulk of the work was in safely providing the team of agents with access to an environment, in this case Jupyter Notebooks. The notebook also records the team's conversation (i.e. reasoning), code and code results, a useful audit trail of the team's activity.

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Watch the team in action...

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