The shift from passive chatbots to active AI agents is one of the most important developments in the current AI landscape. Where a standard Large Language Model (LLM) primarily answers questions and generates text, an agentic AI performs tasks autonomously. It queries databases, calls APIs, evaluates its own output, and plans next steps to achieve a goal.
Building a robust agent places fundamentally different demands on the underlying AI model. A model that writes beautiful poems can fail miserably when it needs to generate a strictly formatted JSON payload for an API request. In this article, we discuss the core qualities that make a model suitable for agent applications, how you can test these properties yourself, and we provide a decision framework for different types of agents.
What makes a model 'agent-ready'?
When evaluating models for agent workflows, the focus is on reliability and strictness, rather than solely linguistic creativity. The following four pillars are decisive.
1. Native Function Calling and Tool Use
To perform actions in the real world, a model must be able to call external tools (such as a calculator, a web search engine, or an internal database). This is called function calling or tool use. Older or simpler models attempt to execute the tool call by simply generating text that looks like code. The current generation of 'agent-ready' models is explicitly trained (fine-tuned) to recognize when a tool is needed, select the correct arguments, and temporarily pause execution while waiting for the result.
A crucial aspect here is that the model does not hallucinate arguments. If an API requires a mandatory field customer_id, the model must not make it up, but must actively ask the user for this information, or use a previous tool to look up this ID.
2. Multi-step reasoning
An effective agent thinks ahead. In complex tasks, such as debugging software or planning a trip, a single action is rarely sufficient. The model must be capable of iterative loops: Observe → Reason → Act (often referred to as the ReAct framework).
For these autonomous cycles, the model must be able to plan logical steps without losing track. Models specifically trained on deep logic and mathematics typically score better here. Want to know more about how these specific capabilities are measured? Check out our overview of reasoning models compared.
3. Exact format strictness (JSON compliance)
When an agent communicates with software, this usually happens via JSON (JavaScript Object Notation). If an LLM provides a perfectly structured response, but accidentally places the text "Here is the JSON you requested:" before the curly braces, a parser or API will crash immediately. Agent models must respect syntax instructions 100%. So-called Structured Outputs or JSON Mode functionalities in model providers' APIs help with this, but the base model must be capable enough to fill in the schema correctly.
4. Residual memory and context management
Agent tasks often require long conversation histories, especially when the agent repeatedly calls tools and receives long contexts of API responses back in the prompt. Over time, a model can "forget" what the initial instruction was, a phenomenon we often see in the middle of long texts. A reliable agent model has high "recall" across the entire span of its available working memory. For a deeper understanding of this, read our article on context window explanation.
Testing a model for agent suitability yourself
Never blindly build an application based on marketing claims or general benchmarks like MMLU (Massive Multitask Language Understanding). These scores say little about how the model will perform in your specific architecture. For a fundamental understanding of the architecture surrounding these models, you can consult our introduction to agentic workflows. Testing it yourself is crucial. Use the following testing strategies for your model evaluation:
- The Golden Dataset: Create a collection of 50 to 100 representative prompts from your domain. Define exactly which tool should be called and with which arguments for each prompt. Use scripts to automate how often the model produces the correct JSON structure.
- Negative testing: Give the model a task for which none of the available tools are relevant. A good agent model will refuse to use a tool and instead ask for clarification. A weak model will attempt to use an irrelevant tool.
- Error Recovery: Simulate a failing API. Intentionally return a "404 Not Found" or "Invalid parameter" error to the model in response to its tool call. Test whether the model understands what went wrong and whether it makes a new, corrected attempt, or if it panics and sends the error message to the end user.
Decision framework: Which model for which agent task?
Not every agent needs the most expensive, most advanced model. In practice, complex agentic systems are built from multiple, collaborating agents (multi-agent systems), where the models are tailored to the specific subtask. This reduces costs and increases speed.
1. The Router Agent or Triage Agent
The task: Classifying incoming queries and deciding to which subsystem or other agent the query should be forwarded. It does not make complex decisions, but merely routes.
The ideal model: Small, extremely fast, and cheap. The model needs minimal reasoning capacity, but must be able to classify in JSON format or enums (enumerations) 100% error-free.
Real-world examples: Optimized variants such as Llama 3 (8B) or smaller open-weights models are perfect for this task. See also our guide on small models on-device for locally running alternatives that minimize latency.
2. The Research or Extraction Agent
The task: Sifting through large amounts of text (such as company documents, web pages, or internal databases via RAG) to find and structure specific data points.
The ideal model: A model with a very large context window (more than 128k tokens) and excellent 'Needle-in-a-Haystack' performance. Speed is less important than reading accuracy. It must not hallucinate facts and must quote exactly.
Real-world examples: Models in the Claude 3 family (such as Haiku or Sonnet) or Gemini 1.5 Pro are widely used for heavy document processing thanks to their massive context acceptance.
3. The Code and Execution Agent
The task: Independently writing scripts, executing Python code in a secure sandbox, or running complex data analyses (such as OpenAI's Advanced Data Analysis feature).
The ideal model: A model heavily trained on programming languages and syntax. It must handle error messages from a compiler or interpreter excellently, and be able to use them to iteratively debug its own code.
4. The Orchestrator (The 'Main Agent')
The task: Breaking down a large, vague goal into subtasks (task decomposition), delegating work to sub-agents, and synthesizing the final results.
The ideal model: No corners should be cut here. The orchestrator acts as the brain of the operation. You need the highest possible intelligence (frontier models). The model must be able to reason abstractly, maintain the big picture, and correct course when sub-agents fail.
Real-world examples: OpenAI's GPT-4o, Anthropic's Claude 3.5 Sonnet, or the most powerful models from the Gemini Ultra/Pro series.
Pitfalls of Deploying LLMs as Agents
Even with the right model, you will inevitably run into specific challenges when bringing agent workflows into production.
- Infinite Loops: An agent that keeps calling an API with an incorrect parameter and does not understand the error can get stuck in a loop. This is dangerous and expensive. Always build a hard max_iterations limit into your code.
- Context Degradation (The Forgetful Agent): If an agent has executed 15 steps, the prompt by then contains all intermediate API responses, logs, and reasoning steps. The model can get confused as a result. A solution is to implement an intermediate summarization step (memory compression), where you ask the model to compactly summarize the current state before the next step starts.
- Unexpected Cost Explosions: Because agents iterate autonomously, they invisibly generate and consume massive amounts of tokens. A simple task can result in 10 consecutive LLM calls. If you scale this to thousands of users, costs rise exponentially. It is therefore business-critical to think carefully about hosting models or choosing the right pricing model. You can read more about this in our analysis of Total Cost of Ownership (open vs closed).
Conclusion and next steps
Choosing an AI model for an agent is fundamentally different from choosing a model for a chatbot. The emphasis shifts from textual fluency to logic, format reliability (JSON), and the correct use of tools. Start small: first build a single agent function with tight evaluation, rather than immediately launching an entire network of autonomous bots.
Use our decision framework to distribute computing power efficiently: deploy small, fast models for routing and classification, and save the expensive "frontier" models for orchestration and complex problem-solving. By working in a structured and data-driven manner, you create agents that do not just promise to do things, but actually and reliably execute them.