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Think of a AI agent Like an autonomous assistant. He understands messages, decides what to do, and executes actions. Examples: answering questions, summarizing emails, and scheduling meetings.

This agent connects to tools. APIs, databases, Google Calendar, and WhatsApp are common. This allows it to act in the real world with confidence and context.

Limitations arise when we ask too much of them. A single agent can become slow, confused, and expensive. It makes more mistakes when it needs to cover very different tasks.

How they work in practice and where to apply them.

In practice, the agent receives the user's input. It reads the context, chooses an action, and calls the... tool Correct. Deliver the result and record what happened.

The applications are broad and straightforward. Customer service, call triage, conversation summarization, and scheduling. Administrative and operational routines are also included.

Many agents vs. Multi-agents

Having many agents doesn't mean having a system. multi-agent. Several isolated agents don't communicate and create silos. This seems efficient, but it turns into chaos in the operation.

Multi-agent systems are a different story. Specialized agents share data and context. They collaborate to solve complex workflows as a team.

Types of architecture

Orchestrator or Supervisor

There is a main agent. He sees the whole picture, makes decisions, and delegates tasks. He is simple to control, but he is the single point of failure.

Network of Agents (decentralized)

There is no single boss. Agents exchange messages and make decisions together. This provides flexibility, but debugging can be more difficult.

Hierarchical in layers

Strategic layers at the top. Operational layers at the base execute actions. Helps to scale and separate responsibilities.

Custom architecture

It blends previous elements as needed. It balances control, flexibility, and specialization. It's the most common approach in real-world projects.

Advantages: modularity, specialization, and cost.

• Modularity: Each agent is an independent unit. You can swap, test, and update parts without breaking the whole. Maintenance becomes predictable and secure.
• Specialization: One agent, one task. Fewer errors, more performance, and higher quality. Smaller models can be used for simple tasks.
• Cost efficiency: You pay for what you need. Lightweight models quickly solve the basic needs. Larger models are only used when they are essential.
• Reuse: Compose agents in new projects. A summary agent can serve multiple systems. This speeds up deliveries and reduces rework.
• Simpler debugging: Isolate the problem by agent. Inspect specific logs and entries. Fix it quickly without bringing everything to a standstill.

When to use (and when to avoid)

Use multi-agent architecture when there are distinct tasks, different sectors, multiple integrations, and interconnected steps. When the project grows, the architecture shines.

Avoid this if the flow is linear and repetitive. A single, well-configured agent may suffice. Added complexity is costly and adds latency.

Practical example in e-commerce with multiple agents.

Imagine a customer starting a purchase. customer service agent It understands needs and collects data. Then it sends context to the next agent.

O stock agent Check availability. If it's okay, activate the... payment agent. He sends the link and confirms the payment.

Then comes the logistics agent. It generates the tracking code and organizes the delivery. Everyone shares data to maintain a coherent workflow.

ResultAgility and scale. Each agent does what they do best. The entire team functions as a coordinated organism.

Precautions and risks when implementing

• Cost: More agents generate more API calls. Without planning, the bill grows quickly. Monitor usage and set limits.
• Latency: Conversations between agents add to delays. Design for parallelism and timeouts. Avoid unnecessary dependencies between steps.
• Complexity: Don't complicate the simple. If a single agent solves the problem, don't multiply agents. Prioritize clarity over sophistication.
• Prompts and protocols: Define a clear structure. Who speaks to whom, in what format, and in what context. Poorly written prompts undermine quality..
• Observability: Record inputs, outputs, and decisions. Keep logs by agent and by transaction. This reduces the time needed to correct errors.

Closing

Multi-agent architectures deliver coordination, scale, and control. They are ideal for processes with multiple functions and integrations. Choose the right architecture and move forward with confidence.

Example of a recommended stack

• Models Orchestrator: GPT-5 Thinking. Utilities: GPT-5 mini/nano for simple tasks. Embeddings: text-embedding-3-large; OSS: Llama 3.1/Mistral.
• Orchestration LangGraph or AutoGen for multi-agent coordination. Queues: Redis Streams or RabbitMQ. Scheduler for routines and SLAs.
• Memory and RAC Vector DB: Pinecone, Weaviate, or pgvector. Section indexing and source versioning. Citations with confidence scores.
• Tools and integrations Whatsapp via Twilio or Gupshup. CRM: Notion, Pipedrive or HubSpot. Email, Slack, Google Calendar and Sheets.
• Data and Infrastructure Transactional database: Postgres/Supabase. S3-compatible storage for attachments. Backend: FastAPI (Python) or Node/Express.
• Observability and security Tracing: OpenTelemetry and LangSmith. PII masking, RBAC and vault/Doppler. Cost alerts and agent-based auditing.
• Delivery Front-end web development in Next.js. Webhooks for events and automations. End-to-end testing with Playwright and API contracts.

FAQ: AI Multi-Agents

The advancement of language models has transformed the way we interact with technology, and the GLM 4.5 It emerges as an important milestone in this evolution.

Developed by the Zhipu AI team, this model has been gaining global recognition by offering a powerful combination of computational efficiency, structured reasoning, and advanced support for artificial intelligence agents.

For developers, companies, and AI enthusiasts, understanding what GLM 4.5 is and how it compares to other standards is crucial. LLMs It is essential to take full advantage of its features.

What is GLM 4.5 and why does it matter?

O GLM 4.5 It is a Mixture of Experts (MoE) type language model, with 355 billion total parameters and 32 billion active parameters per forward pass.

Its innovative architecture allows for the efficient use of computing resources without sacrificing performance in complex tasks.

The model is also available in lighter versions, such as the GLM 4.5-Air, optimized for cost-effectiveness.

Designed with a focus on reasoning tasks, code generation, and interaction with autonomous agents, GLM 4.5 stands out for its support for... hybrid way of thinking, which alternates between quick responses and in-depth reasoning on demand.

Technical characteristics of the GLM 4.5

The technical advantage of GLM 4.5 lies in its combination of optimizations to the MoE architecture and improvements to the training pipeline. Among the most relevant aspects are:

Intelligent and balanced routing

The model employs sigmoid gates and QK-Norm normalization to optimize routing between specialists, ensuring better stability and utilization of each specialized module.

Extended context capability

With support for up to 128,000 entry tokens, The GLM 4.5 is ideal for long documents, extensive code, and deep conversation histories. It is also capable of generating up to 96,000 output tokens.

Muon Optimizer and Grouped-Query Attention

These two advancements allow GLM 4.5 to maintain high computational performance even with the scalability of the model, benefiting both on-premises and cloud deployments.

GLM 4.5 Tools, APIs, and Integration

The Zhipu AI ecosystem facilitates access to GLM 4.5 through APIs compatible with the OpenAI standard, as well as SDKs in various languages. The model is also compatible with tools such as:

• vLLM and SGLang for local inference
  
• ModelScope and HuggingFace for use with open weights
  
• Environments with OpenAI SDK compatibility for easy migration of existing pipelines
  

To see examples of integration, visit the official documentation for GLM 4.5.

Real-world applications: where GLM 4.5 shines

GLM 4.5 was designed for scenarios where generic models face limitations. Among its applications are:

Software Engineering

With high performance in benchmarks such as SWE-bench Verified (64.2) and Terminal Bench (37.5), it positions itself as an excellent option for automating complex coding tasks.

Assistants and Independent Agents

In the tests TAU-bench and BrowseComp, GLM 4.5 outperformed models like Claude 4 and Qwen, proving to be effective in environments where interaction with external tools is essential.

Complex data analysis and reporting.

With its strong contextual capabilities, the model can synthesize extensive reports, generate insights, and analyze lengthy documents efficiently.

Comparison with GPT-4, Claude 3 and Mistral: performance versus cost

One of the most notable aspects of the GLM 4.5 is its significantly lower cost compared to models such as... GPT-4, Claude 3 Opus and Mistral Large, even though it offers comparable performance across various benchmarks.

For example, while the average cost of generating tokens with GPT-4 can exceed US$ 30 per million tokens generated, The GLM 4.5 operates with averages of US$ 2.2 per million output, with even more affordable options such as GLM 4.5-Air for only US$ 1.1.

In terms of performance:

• Claude 3 It excels in linguistic reasoning tasks, but GLM 4.5 comes close in mathematical reasoning and code execution.
• Mistral It excels in speed and local compilation, but doesn't reach the contextual depth of 128k tokens like GLM 4.5.
• GPT-4, Although robust, it demands a high price for performance that in many scenarios is matched by GLM 4.5 at a fraction of the cost.

This cost-effectiveness positions the GLM 4.5 as an excellent choice for startups, universities, and data teams looking to scale AI applications on a budget.

Performance comparison with other LLMs

GLM 4.5 not only competes with the big names in the market, but also surpasses them in several metrics. In terms of reasoning and execution of structured tasks, it achieved the following results:

• MMLU-Pro: 84.6
  
• AIME24: 91.0
  
• GPQA: 79.1
  
• LiveCodeBench: 72.9
  

Source: Official report from Zhipu AI

These numbers are clear indicators of a mature model, ready for large-scale commercial and academic use.

Future and trends for GLM 4.5

Zhipu AI's roadmap points to even greater expansion of the product line. GLM, with multimodal versions such as the GLM 4.5-V, which adds visual input (images and videos) to the equation.

This direction follows the trend of integrating text and images, which is essential for applications such as OCR, screenshot reading, and visual assistants.

Ultra-efficient versions are also expected, such as the GLM 4.5-AirX and free options like GLM 4.5-Flash, which democratize access to technology.

To keep up with these updates, it is recommended to monitor the official project website.

A model for those seeking efficiency with intelligence.

By combining sophisticated architecture, versatile integrations, and excellent practical performance, the GLM 4.5 It stands out as one of the most solid options in the LLM market.

Its focus on reasoning, agents, and operational efficiency makes it ideal for mission-critical applications and demanding business scenarios.

Explore more related content at Agent training course with OpenAI, Learn about integration in Make course (Integromat) and check out other options for AI and NoCode training programs.

For those seeking to explore the state-of-the-art in language models, GLM 4.5 is more than just an alternative—it's a step forward.