AI agents are reshaping how businesses handle work—from answering support tickets to automating research, generating code, or coordinating tasks across systems. But what’s actually happening under the hood? How do these agents operate, make decisions, and interact with people and tools?
In this post, we’ll break down the inner workings of AI agents—from how they’re given goals to how they plan, remember, learn, and execute—all using modern language models and tools. Let’s dive in.
Goal Initialisation and Planning
Every AI agent starts with a goal—either set by a human, triggered by a system event, or derived from a larger objective. Once the goal is clear, the agent builds a plan.
This involves:
Understanding the end state: e.g., “Book a meeting,” “Resolve a customer issue,” or “Summarise this document.”
Breaking it into sub-goals or steps: like retrieving context, choosing the right tools, asking clarifying questions, or triggering follow-ups.
Evaluating dependencies: Does the agent need access to an external API, another agent, or human input to move forward?
This planning resembles human reasoning, but it’s guided by language-based instructions and logic encoded in prompts or learned behaviours.
Role of Memory: Short-term, Long-term, Episodic, and Consensus
One of the biggest shifts in AI agents today is how they remember.
Short-term memory keeps track of the current conversation or task context—like remembering what a user just said.
Long-term memory stores persistent knowledge, such as company policies, tool instructions, or product information.
Episodic memory allows agents to recall specific past interactions—what happened in the last meeting, or how a previous case was resolved.
Consensus memory is more collaborative. It allows multiple agents (or one agent and a human) to co-create and share memory—essential in multi-agent systems.
This layered memory system makes agents more consistent, personalised, and useful over time—especially in longer workflows.
Tools and Tool Usage: A Built-in Capability
LLMs can do a lot with just words—but not everything.
To act in the world, agents need tools. That includes:
APIs (e.g., sending emails, updating records)
Databases (fetching or writing data)
Calculators or search tools
Third-party apps like CRMs, analytics platforms, or ticketing systems
Tool usage is often orchestrated using Toolformer-like methods: the agent learns when and how to use a tool based on the task and context. It doesn’t guess—it calls the right tool with structured inputs, handles outputs, and continues reasoning.
The result? An agent that doesn’t just talk—but does.
Model Architecture: LLMs as Agent Brains
At the core of most AI agents is a Large Language Model (LLM)—like GPT-4o, Claude, LLaMA, or Gemini. Think of it as the brain.
The model:
Processes input (natural language or structured data)
Applies reasoning and planning
Generates responses, actions, or tool calls
Advanced architectures include multi-model memory, attention mechanisms, and agent-specific tuning that allow different agents to behave in specialised ways—like a legal assistant vs. a growth analyst.
These “brains” can also be hot-swapped—you can run the same agent logic on different LLMs depending on cost, accuracy, or data policies.
Persona and Communication Style
AI agents don’t just solve problems—they interact with people. That’s why persona design matters.
You can shape how an agent behaves by controlling:
Tone (formal, friendly, technical, etc.)
Expertise level (basic explainer vs. senior analyst)
Decision-making style (conservative, exploratory, collaborative)
Response formatting (tables, charts, summaries, markdown, etc.)
This isn’t cosmetic. A well-matched persona builds trust, improves clarity, and makes the AI feel like part of your team—not just a chatbot.
How cognipeer Works
cognipeer brings all these elements together into one powerful, flexible platform. It lets businesses build and deploy AI agents that are more than bots—they’re autonomous peers.
Here’s how:
Goal setting: Define workflows, objectives, or actions that trigger your agents.
Tool integration: Connect any internal system or SaaS tool using APIs or native connectors.
Custom memory: Configure short-term, long-term, and even cross-agent memory types.
Agent logic: Choose models (like GPT-4o or Claude), fine-tune prompts, and define multi-step behaviours.
Multi-agent orchestration: Build teams of agents that collaborate—e.g., a Sales Peer passing data to a Finance Peer.
Persona control: Tailor tone, expertise, and communication styles per agent.
Whether you’re automating support, enhancing R&D, or coordinating complex processes, cognipeer gives you the control to do it your way—with full visibility and enterprise-grade scalability.
Final Thoughts
AI agents are no longer just a trend—they’re a transformative layer in modern business. By combining memory, planning, communication, and tool usage, today’s agents can work like humans—only faster, more scalable, and always available.
And with platforms like cognipeer, building and deploying these agents is no longer a technical barrier—it’s a strategic advantage.
→ Ready to see what agentic automation can do for your business? Explore cognipeer