AI Agent Architecture: Components, Patterns, and Design
AI agent architecture explained: the core components, the patterns that fit each job, and how to design tools, memory, and approvals for production.
An AI agent architecture is a model running in a loop: it reads state, picks a tool, checks the result, and decides whether to continue. Your design work is everything around that loop: tools, memory, permissions, and approval gates. Melaya is built as that surrounding layer. It is bring-your-own-model, so Claude, GPT, Gemini, DeepSeek, Mistral, Qwen, Ollama, or LM Studio can sit in the loop, and every consequential action (publishing, sending, paying, writing to connected apps) runs behind human approval.
TL;DR
- An agent differs from a standard LLM app in who drives control flow: the model chooses the next step at runtime, not your application code.
- Core components: perception, reasoning/planning, memory, and tools/actions. Wrap them in permissions, traces, and approvals.
- Start with a single agent. Move to sequential, coordinator, or multi-agent patterns only when the task needs it.
- Treat credentials and approvals as architecture, not add-ons: scoped, short-lived access and human sign-off on irreversible actions.
- Keep the model swappable. Tools and workflows should survive a model change.
What AI agent architecture is
It is the structural design of a system in which a language model perceives state, reasons over a task, invokes tools, evaluates results, and loops until the task is done. A conventional LLM application is request and response: the user sends a prompt, the model answers, the interaction ends, and your code owns the sequence.
CodeBridge splits this into three levels:
| Level | Who decides the path | Example |
|---|---|---|
| LLM feature | Your code | Summarize one ticket |
| Workflow | Your code, with model judgment at set steps | Classify, then route, then draft |
| Agent | The system, at runtime | Investigate an incident, choose tools, adapt |
The model's control over sequencing is the source of an agent's power and of its new failure modes. That is why the architecture, not the prompt, determines whether it is safe to run.
Not everything should be an agent. Redis notes that a direct API call returning account status in under 10 ms gains nothing from an extra LLM step, which adds hundreds of milliseconds and spends tokens on every call. Use deterministic code where the path is known.
The components of an AI agent
An academic survey of LLM agents describes four systems working together:
- Perception. Captures environment data (a page, a database row, a screen) and turns it into a representation the model can use.
- Reasoning and planning. Forms a plan and adjusts it from feedback.
- Memory. Stores knowledge that is not in the model weights.
- Action. Turns decisions into operations such as API calls or code.
NVIDIA's introduction lists the same set as agent core, memory module, tools, and planning module. Redis adds reflection loops, where the agent evaluates outcomes before continuing.
For production you add three more layers around those four:
- Permissions: what the agent may read, modify, or trigger.
- Traces: a record of every step so you can replay a run.
- Approvals: human gates on consequential actions.
A useful mental model: your AI is the brain, Melaya is the controlled pair of hands.
Architecture patterns and when each fits
HoneyHive's pattern survey lists the common ones. Match the pattern to task complexity, latency tolerance, cost budget, and how much human involvement you need.
| Pattern | How it works | Fits |
|---|---|---|
| Single agent | One model with tools | Prototypes, narrow tasks |
| Sequential | Specialists in fixed order | Repeatable processes |
| Parallel | Subagents work at once | Independent subtasks |
| Loop | Repeat until a stop condition | Iterative refinement |
| Review and critique | Generator plus checker | Quality assurance |
| Coordinator | Central agent routes to specialists | Adaptive routing |
| Hierarchical | Multi-level delegation | Complex, ambiguous problems |
| Swarm | All-to-all communication | Emergent coordination |
| Human-in-the-loop | Pause at checkpoints | High-stakes approvals |
Default choice: a single agent with well-defined tools. Add structure when you can name the failure it fixes. A sequential pipeline fixes inconsistent ordering. A critic fixes unchecked output. A coordinator fixes a generalist agent drowning in tool choices.
Single agent or multi-agent
Anthropic's guide reports internal research in which multi-agent systems beat single agents by 90.2% on complex tasks that pursue several independent directions at once. It recommends them for open-ended problems, specialized expertise that would overwhelm a generalist, and broad queries across multiple directions. For a linear task, one agent is cheaper and easier to debug.
Melaya's visual Agent Builder combines models, tools, memory, schedules, approvals, triggers, and multi-agent pipelines in one canvas, so moving from one agent to a pipeline is a configuration change. Our multi-agent orchestration guide walks through the patterns, and the AI orchestration overview covers how to build them.
Designing the layers
Tools
CodingCrafts recommends narrow tool definitions: one clear operation each, typed inputs and outputs, and a permission check. A tool called update_record that accepts anything is a liability. Tools called read_invoice and draft_invoice_update are not.
Tools are also where agents touch the real world. Melaya's Browser Control, Mobile Agents, and Computer Control let approved models operate websites, Android applications (Android only, not iOS), and desktop software through the Melaya desktop app. The Melaya MCP Server exposes the same tools to ChatGPT, Claude, Cursor, Codex, and other MCP-compatible clients.
Memory
Separate short-term context (the current task) from long-term storage (facts that persist across sessions). Write rules for what can be stored, updated, retrieved, and forgotten. Without them, memory becomes an unaudited copy of your data.
Model
Treat the model as a replaceable part. Melaya is bring-your-own-model: you connect any cloud or local model (Claude, GPT, Gemini, DeepSeek, Mistral, Qwen, Ollama, LM Studio) or a model router, and tools and workflows stay in place when you change it. Melaya does not sell or require its own model for agents. For private deployments, running a local model through Ollama or LM Studio keeps inference on your own infrastructure.
Identity and credentials
The Cloud Security Alliance describes a shift from agents holding credentials to agents requesting them, with per-task authorization that is scoped and time-bound. It also describes just-in-time provisioning with short-lived tokens, typically with TTLs of 5 to 15 minutes for routine operations. Red Hat warns that failures often start when identities merge into a single shared agent credential, and recommends separate identities so every step is attributable. The same principle of shrinking standing access is covered in this guide to zero trust for distributed teams.
Red Hat also lists what orchestrator-side authorization should evaluate before a tool runs: the requested operation, user identity and session, resource scope, data sensitivity, risk level, and whether confirmation or approval is required. Melaya applies granular permissions, so you decide per agent which tools and resources it can reach.
Human approval as a design element
CodingCrafts says to require human input when the cost of a wrong choice justifies it: changing customer records, triggering financial transactions, deleting data. Omnithium describes three patterns: human-on-the-loop (agent acts, a human can interrupt within a window), human-in-the-workflow (humans act at checkpoints), and fully autonomous for low-stakes, high-volume tasks with mature monitoring.
A risk-tiered approach works well: high-severity actions need synchronous approval, read-only actions are auto-allowed with full logging, and prohibited actions are hard denied. Tiers should come from the properties of the action, not from model confidence.
One engineering detail matters: pausing mid-run means suspending the workflow. Omnithium advises serializing state to a checkpoint store before requesting approval so execution resumes from the exact point.
In Melaya, every consequential action, meaning publishing, sending, paying, or writing to a connected app, runs behind human approval. Runs are traceable and replayable, with execution traces you can inspect after the fact.
A build checklist
- Write the task and its stop condition. If you cannot state when the agent is done, a loop will not finish.
- Decide if it is an agent at all. Known path: workflow. Unknown path: agent.
- List tools, one operation each, with typed inputs and outputs.
- Tier the actions: read-only, reversible write, irreversible or financial. Put approval on the last two.
- Scope credentials per task and per identity, short-lived where you can.
- Split memory into task context and durable storage with retention rules.
- Pick a model, and keep it swappable. Test the same run on a second model.
- Turn on traces, then replay failed runs before widening permissions.
- Add a second agent only for a named problem: review, parallel work, or routing.
Where Melaya fits
If you would rather configure this architecture than assemble it, Melaya packages the layers above: Agent Builder, Melaya Assistant across connected business systems, Browser Control, Mobile Agents, Computer Control, and the MCP Server, with native integrations such as Odoo and independent security audits. Pricing is published per product: Browser Agents, Mobile Agents, and Melaya Assistant are $9.99 per month each, Melaya Marketing is $14.99 per month, and platform plans run from Sandbox at $0 per month through Outpost at $20 per month, Forge at $49 per month or $469 per year, and Bastion at $129 per month or $1,239 per year, to Citadel from $490 per month. See the pricing page.
For browser-level agents, read how browser agent security is handled, or see the operations crew for a worked multi-agent setup.
Our perspective
Most agent failures we would worry about are not model failures. They are architecture failures: a tool that accepts anything, a shared credential, or an action that fires with no one watching.
Our position is that the model should be the most replaceable part of your stack. Models change every few months. Your tools, permissions, approval rules, and traces are the assets that should last. That is why Melaya is bring-your-own-model and works with cloud and local models alike.
Your AI is the brain, Melaya is the controlled pair of hands: granular permissions decide what it can touch, human approval gates every consequential action, and every run leaves a trace you can replay.
Melaya Team
FAQ
What are the main components of an AI agent?
Four: perception, reasoning and planning, memory, and tools that act. Production systems add permissions, execution traces, and human approval around them. Together they let the agent read state, decide, act, and check the result.
How is an AI agent different from a workflow?
In a workflow your code defines the sequence and branching, with the model judging at set steps. In an agent the model chooses the next step and its tools at runtime. Use workflows for known paths and agents for open-ended ones.
Do I need multiple agents?
Not by default. Start with one agent and well-scoped tools. Add agents when the task splits into independent directions, needs specialist roles, or needs a separate reviewer checking output.
Can agents run on local or open-weight models?
Yes. Melaya works with local models such as Ollama and LM Studio, as well as Qwen, Mistral, and DeepSeek. You bring the model, and the tools, memory, and approvals stay the same when you switch.
Where should humans approve?
Where a wrong action is costly or irreversible: changing customer records, moving money, deleting data, publishing, or sending. In Melaya, these consequential actions run behind human approval.
Sources
- AI Agent Architecture, Logic Grid Studio
- Agentic AI vs LLM, CodeBridge
- Agentic AI architecture patterns for production systems, Redis
- Fundamentals of Building Autonomous LLM Agents, arXiv
- Introduction to LLM Agents, NVIDIA
- AI agent architecture patterns, HoneyHive
- Building Effective AI Agents, Anthropic
- How to Create AI Agents, CodingCrafts
- Human-in-the-Loop Patterns, Omnithium
- Long-Running Governed AI Agents, IoT Digital Twin PLM
- Zero Trust for Securing Agentic AI, Cloud Security Alliance
- AI security: Identity and access control, Red Hat
- Zero Trust vs VPN for Distributed Teams
Recommended
- AI Agent Governance Framework
- Enterprise AI Agent Platform
- AI Agent Builder: Visual, No-Code, Free to Start
- How to Build Enterprise AI Agents in 4 Steps
Frequently asked questions
What are the main components of an AI agent?
Four: perception, reasoning and planning, memory, and tools that act. Production systems add permissions, execution traces, and human approval around them. Together they let the agent read state, decide, act, and check the result.
How is an AI agent different from a workflow?
In a workflow your code defines the sequence and branching, with the model judging at set steps. In an agent the model chooses the next step and its tools at runtime. Use workflows for known paths and agents for open-ended ones.
Do I need multiple agents?
Not by default. Start with one agent and well-scoped tools. Add agents when the task splits into independent directions, needs specialist roles, or needs a separate reviewer checking output.
Can agents run on local or open-weight models?
Yes. Melaya works with local models such as Ollama and LM Studio, as well as Qwen, Mistral, and DeepSeek. You bring the model, and the tools, memory, and approvals stay the same when you switch.
Where should humans approve?
Where a wrong action is costly or irreversible: changing customer records, moving money, deleting data, publishing, or sending. In Melaya, these consequential actions run behind human approval.