Introduction
Imagine you’re using an AI assistant to handle important communications for your company—like responding to emails, drafting press releases, or managing event invitations. Now, imagine if that AI assistant gives the wrong answer or rambles off-topic. Not exactly ideal, right?
The truth is, many AI models can occasionally “hallucinate,” generating responses that don’t make sense or aren’t grounded in facts. While this might be fun in casual conversations, it’s a serious problem when you’re communicating on behalf of a business. That’s why our team at Adastra set out to create an AI agent that not only responds accurately and safely, but also sounds friendly and human.
The Problem
Traditional AI models often struggle with:
- Inaccurate Answers: They can generate random or incorrect responses.
- Limited Memory: They forget previous interactions due to context window limits.
- High Costs: Next-generation “reasoning” models are powerful, but can be very expensive to operate.
For businesses that need to use AI for daily tasks—like PR, event planning, and customer engagement—unreliable or costly solutions are simply not acceptable.
The Opportunity
Despite these challenges, 2025 has been hailed as the “Year of the AI Agent,” thanks to breakthroughs in model design. Companies like OpenAI and Deepseek are pioneering new approaches such as “Test-Time Computing” or “Chain-of-Thought” (read more about the concept here).
Here’s the catch: these advanced solutions can come with a hefty price tag—sometimes up to ten times more expensive than earlier AI models. This has left many teams, including ours, seeking alternative ways to achieve high-quality results without overspending.
Our Approach: Model + Data + Tooling + Prompting
We discovered that building an effective AI agent isn’t just about having the most advanced model. It’s about combining the right pieces:
- A Good Model: A large language model (LLM) to handle the core language understanding and generation.
- Data & Tools: Relevant data for context, plus external “tools” the AI can call on (like a database or event-scheduling system).
- Prompt Engineering: Thoughtful prompts that guide the AI’s behavior and keep it on track.
Through the open-source community, we found two exceptional resources:
- mem0: An open-source “memory layer” that lets an AI agent store and retrieve information across long conversations (Reference link).
- Thinking Claude: A prompt engineering template that helps models go through a step-by-step thought process before producing a final response (Reference link).
With these tools, we’ve given our AI agent the ability to “think out loud” (internally) and maintain context over extended dialogues, without repeatedly sending huge amounts of text to the model.
The Solution in Action
1. Enhanced Memory (with mem0)
Traditional AI models have a limited “context window,” meaning they forget earlier parts of a conversation if it runs too long. Using mem0, we store important pieces of the conversation externally. When we need to recall a point, mem0 retrieves the relevant information and feeds it back into the AI model.
This approach ensures our AI agent “remembers” details—like a stakeholder’s name, the event date, or any specific requests—without driving up computing costs.
2. Thinking Claude Prompt Template
Inspired by “chain-of-thought,” the Thinking Claude template instructs the AI to outline its reasoning steps privately (in what we call a “think” phase) before giving you a clear, polished response. This process significantly reduces errors and makes the final output more coherent.
So instead of one-shot, “hasty” answers, our AI agent essentially “takes a moment to think things through” and then responds.
3. Tool Integration
We’ve also defined a tool interface that the AI can call whenever it needs additional data—like verifying event schedules or pulling up stakeholder information. These tools work behind the scenes to provide real-time data that the AI can use in forming its answers.
The Results
- More Accurate Responses: By combining mem0’s memory layer with step-by-step prompting, we’ve drastically reduced AI hallucinations.
- Human-Like Interaction: Users report that conversations feel more natural—like talking to a real person who listens and remembers details.
- Cost-Effective: We avoided the higher fees associated with next-generation “reasoning models” by leveraging open-source tools and clever prompt strategies.
- Better Reliability: Inspecting the AI’s hidden “thinking steps” shows us how it arrives at conclusions, giving us confidence in the final output.
Try It Yourself
We believe that an AI agent should represent your company’s values—reliable, knowledgeable, and approachable. If you’re curious about how this works, we invite you to explore our dashboard at Adastra.
Whether you’re managing PR for a multinational corporation or planning local community events, our AI agent is here to support you. We’d love to hear your feedback and ideas on how we can make it even better.
Conclusion
Building an AI agent that “thinks before it speaks” has transformed the way our system handles corporate and event communications. By pairing mem0’s long-term memory with Thinking Claude’s step-by-step reasoning templates, we’ve created a solution that is both reliable and cost-effective.
If you have any questions about our approach or would like to discuss how it can work for your organization, reach out through our platform or get in touch with our team. We’re always excited to explore the future of AI with you!
References & Additional Reading:
- Chain-of-Thought Prompting Paper (arXiv)
- mem0 docs – Open-Source Memory Layer
- Thinking Claude – Prompt Engineering Template