AI Agents types
The AI market is currently flooded with buzzwords, fake demos, and companies pretending they invented intelligence because they wrapped a prompt around an API. To understand what is actually happening, you need to separate four different layers:
- Large Language Models (LLMs)
- Coding Assistants
- AI Agents
- AI Agent Platforms
Most people mix them together. That creates confusion and terrible technical decisions.
1. Large Language Models (LLMs)
LLMs are the reasoning engines behind modern AI systems.
Examples include:
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
These models generate text, code, summaries, reasoning chains, and predictions.
But by themselves, they are passive.
A raw LLM cannot:
- Access databases
- Open files
- Modify repositories
- Execute workflows
- Deploy applications
- Control systems
That requires orchestration.
2. Coding Assistants
Coding assistants are AI-powered developer tools integrated into editors or IDEs.
Their main goal is to accelerate software development.
Examples include:
- GitHub Copilot
- Cursor
- Windsurf / Codeium
- Amazon CodeWhisperer
- JetBrains AI Assistant
GitHub Copilot
Copilot is essentially autocomplete on steroids.
Strengths
- Excellent inline code completion
- Good boilerplate generation
- Fast workflow
- Works well in large ecosystems
- Strong Visual Studio and VS Code integration
Weaknesses
- Often shallow reasoning
- Can generate insecure code
- Weak architectural understanding
- Hallucinates APIs
- Repetitive patterns
Copilot improves productivity significantly for experienced engineers.
Junior developers often misuse it and generate technical debt faster.
Cursor
Cursor is one of the strongest AI-native editors right now.
Unlike Copilot, Cursor focuses heavily on repository awareness.
Strengths
- Reads entire repositories
- Multi-file reasoning
- Refactoring support
- Architecture-level understanding
- Agent-like workflows
- Strong conversational editing
Weaknesses
- Sometimes aggressive modifications
- Can over-refactor
- Requires careful review
- Expensive at scale for teams
Cursor feels less like autocomplete and more like collaborative AI engineering.
That is why many senior developers moved from pure Copilot workflows.
Windsurf / Codeium
Codeium positioned itself aggressively as a cheaper alternative.
Strengths
- Good autocomplete
- Lower cost
- Fast suggestions
- Broad IDE support
Weaknesses
- Weaker deep reasoning
- Less mature repository analysis
- Less stable enterprise workflows
Good for individual productivity.
Less convincing for complex enterprise systems.
Claude for Coding
Many advanced developers increasingly use Claude directly instead of IDE-only assistants.
Why?
Because Claude is extremely strong at:
- Long context windows
- Architecture analysis
- Reading massive repositories
- Explaining systems
- Refactoring discussions
Claude often produces better architectural reasoning than raw Copilot-style systems.
Weaknesses
- Less IDE-native integration
- Slower iterative workflow
- Still hallucination-prone
Gemini
Gemini integrates deeply into the Google ecosystem.
Strengths
- Google Workspace integration
- Large context handling
- Multimodal capabilities
- Good search integration
Weaknesses
- Less trusted by many developers
- Inconsistent coding quality
- Product fragmentation
Google has enormous infrastructure strength, but their AI product strategy often feels chaotic.
3. What Was Codex?
Codex was one of the earlier major coding-focused AI systems from OpenAI.
It was trained specifically on code repositories and programming languages.
Codex powered:
- Early GitHub Copilot
- Natural language to code generation
- Early AI programming workflows
Codex was important historically because it proved AI could generate usable software.
But modern GPT-4-class systems largely absorbed Codex capabilities and surpassed them.
Today, when people say “Codex-like systems,” they usually mean:
- AI coding agents
- Repository-aware assistants
- Autonomous coding workflows
The term became broader than the original product.
4. AI Agents
This is where things become much more serious.
A coding assistant suggests code.
An AI agent performs tasks autonomously.
Coding Assistant Example
User:
Generate ASP.NET controller.
Assistant generates code.
Done.
AI Agent Example
User:
Analyze production issue in payment system.
Agent:
- Reads logs
- Queries monitoring system
- Correlates deployment history
- Identifies failing service
- Creates incident
- Suggests fix
- Opens Jira ticket
That is a fundamentally different category of software.
Agent Capabilities
Modern AI agents may:
- Read repositories
- Modify files
- Execute terminal commands
- Run tests
- Query APIs
- Use browsers
- Analyze screenshots
- Control workflows
- Communicate with systems
This moves AI from passive assistance into operational automation.
AI Agent Platforms
Agent platforms provide orchestration infrastructure.
Examples include:
- LangChain
- Semantic Kernel
- OpenAI Assistants API
- AutoGen
- CrewAI
LangChain
LangChain became extremely popular because it allowed developers to connect:
- LLMs
- Tools
- Memory
- Vector databases
- Workflows
Strengths
- Huge ecosystem
- Flexible
- Rapid prototyping
- Massive community
Weaknesses
- Overengineered abstractions
- Breaking API changes
- Complexity explosion
- Difficult debugging
A lot of LangChain projects become spaghetti orchestration systems.
Semantic Kernel
Semantic Kernel is heavily oriented toward enterprise .NET ecosystems.
Strengths
- Strong C# integration
- Enterprise-friendly
- Plugin architecture
- Good orchestration concepts
Weaknesses
- Smaller ecosystem
- Less community momentum than LangChain
- Slower experimentation
For .NET-heavy enterprise systems, Semantic Kernel is often cleaner architecturally.
AutoGen
AutoGen focuses heavily on multi-agent collaboration.
Example:
- One agent analyzes logs
- One writes code
- One validates tests
- One reviews architecture
Strengths
- Multi-agent orchestration
- Complex workflows
- Research-oriented flexibility
Weaknesses
- Can become chaotic
- Expensive token usage
- Hard to control deterministically
Multi-agent systems are powerful but often unstable in real production environments.
CrewAI
CrewAI simplified multi-agent concepts.
It became popular because developers wanted easier orchestration.
Strengths
- Easier setup
- Role-based agents
- Simpler workflows
Weaknesses
- Less flexible
- Abstractions can become limiting
- Enterprise scaling challenges
Good for prototypes.
Less proven for massive enterprise systems.
RAG Systems vs Agents
People also confuse RAG systems with agents.
They are not the same.
RAG (Retrieval-Augmented Generation)
RAG focuses on knowledge retrieval.
Flow:
- Search documents
- Retrieve relevant context
- Inject into prompt
- Generate grounded response
Goal:
Reduce hallucinations.
Agent Systems
Agents focus on action execution.
Flow:
- Understand goal
- Plan actions
- Use tools
- Observe results
- Adjust behavior
- Complete workflow
Goal:
Automation.
Why Most Agent Demos Are Fake
Because many “AI agent” startups are glorified scripts.
Reality:
if(keyword == "invoice")
{
callApi();
}
That is not intelligence.
Real agents require:
- Planning
- Tool orchestration
- Context management
- Recovery handling
- Security boundaries
- State tracking
Most startups underestimate this massively.
The Real Enterprise Problem
The hardest part of AI is not the model.
The hardest parts are:
- Integration
- Data quality
- Permissions
- Reliability
- Observability
- Governance
- Security
- Workflow orchestration
That is why experienced architects still matter enormously.
Weak teams think AI eliminates architecture.
Reality:
AI systems require even stronger architecture discipline.
AI Agents in Software Engineering
This is probably where disruption hits hardest first.
Future development workflows may include:
- AI code review agents
- Autonomous testing agents
- Deployment agents
- Security auditing agents
- Architecture analysis agents
- Dependency upgrade agents
But fully autonomous coding is still unreliable.
Current AI systems are strongest when supervised by experienced engineers.
Industrial and Enterprise Future
The biggest long-term winners will likely not be chatbot companies.
They will be companies integrating AI deeply into:
- Manufacturing
- Healthcare
- Logistics
- Finance
- Enterprise operations
- Industrial automation
Especially systems combining:
- AI
- IoT
- Edge computing
- Event-driven architecture
- Distributed systems
That is where real operational value exists.
Final Thoughts
The AI industry is currently in a phase similar to the early internet boom.
There is real transformation happening.
There is also unbelievable nonsense.
Most companies today are not building intelligence.
They are building interfaces around existing models.
The durable advantage will not come from merely calling GPT APIs.
It will come from:
- Deep domain expertise
- Workflow ownership
- Enterprise integration
- Reliable orchestration
- Security
- Architecture quality
Eventually, everyone will have access to similar models.
Very few companies will know how to properly build serious systems around them.
References
https://www.termdock.com/en/blog/free-ai-cli-tools-ranked
https://www.reddit.com/r/vibecoding/comments/1oa9d5o/best_ai_coding_tool_gemini_cli_codex_or_claude/
More Info
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