The short answer
There is no universal "best" LLM for agentic AI. The right model is the one that completes your tasks correctly, calls your tools with the right arguments, stays within your latency and cost limits, and meets your data requirements. Public leaderboards and launch announcements change every few months and rarely reflect your workflow, so the reliable way to choose is to build a shortlist and test it on representative tasks from your own business.
This guide explains what to test, how to compare the results fairly, and what trade-offs to expect.
What an agent asks of a model
A chatbot answers a question. An agent decides which action to take, calls a tool such as a CRM or calendar API, reads the result and decides what to do next. That puts different demands on the model:
- Tool selection and arguments — choosing the right function and filling its parameters correctly.
- Multi-step reasoning — keeping track of the goal across several tool calls.
- Recovering from failures — handling a tool that times out, returns an error or returns nothing.
- Knowing when to stop — asking a person instead of guessing when information is missing or an action needs approval.
- Language coverage — understanding customers in the languages they actually write in, for UAE businesses typically Arabic and English.
All major providers document how tool use works with their models. See the official guides for OpenAI function calling, Claude tool use and Gemini function calling. If your agent connects to many systems, our explainer on MCP in agentic AI covers a standard way to expose tools to models.
Candidates to put on a shortlist
Most teams start with two to four candidates drawn from:
- OpenAI models (GPT family) via the OpenAI API or Azure.
- Anthropic Claude models via the Claude API or major cloud platforms.
- Google Gemini models via the Gemini API or Vertex AI.
- Self-hosted open-weight models run on your own or a private cloud infrastructure.
Within each family, providers offer larger and smaller tiers. Include at least one smaller, cheaper tier in your tests: for many routine agent steps it performs well enough, and the difference only shows up when you measure.
What to test, by workload
| Workload | What to test | Possible trade-off to measure |
|---|---|---|
| Lead qualification and enquiry triage | Correct category, extracted fields, correct CRM tool call, Arabic and English messages | Larger tiers may improve accuracy on messy messages; measure accuracy and cost per enquiry for each candidate |
| Customer support with knowledge base | Answer accuracy against your documents, refusal when the answer is not known, escalation | Longer context may help with large documents; measure retrieval quality, latency and cost |
| Document processing | Field extraction accuracy, handling of scans and mixed languages | Vision capabilities may affect accuracy and cost; measure both and identify documents needing human review |
| Multi-step operations (booking, order updates) | Task-level success across all steps, correct handling of failed tool calls | Planning reliability and response time may differ; measure task-level success and end-to-end latency |
| High-volume simple routing | Accuracy on clear cases, p95 latency, cost per thousand requests | Smaller tiers may reduce latency and cost; measure accuracy and whether any fallback improves outcomes |
How to run a fair comparison
- Collect a representative task set. Take real past cases (anonymised where needed): common ones, difficult ones, and ones that should be escalated. Include Arabic and English examples in the proportion you actually receive. Keep part of the set held out and do not use it while you tune prompts, so the final comparison is not biased toward the examples you optimised for.
- Define success per task, not per message. A task succeeds only if the final outcome is right: correct record created, correct reply, correct escalation.
- Check tool use explicitly. Record whether the model called the right tool with the right arguments, and whether it ever tried an action outside its permissions.
- Inject failures. Make tools time out or return errors and check that the agent retries sensibly, reports the problem or hands over to a person instead of inventing a result.
- Measure latency as p95, not average. Users notice the slow cases. Measure end-to-end time including tool calls.
- Calculate total cost per completed task. Include retries, long prompts, tool results passed back to the model and any fallback calls, not just the list price per token.
- Record exact versions and settings. Note the model version identifier, temperature, system prompt and tool definitions used. Providers update models, and without this record you cannot reproduce or re-check a result later.
- Repeat when something changes. Re-run the held-out set when you change model version, prompt or tools.
This is a method you can apply yourself; it is not a published benchmark, and results from one business will not transfer automatically to another.
Permissions, approvals and data checks
Model choice is only part of reliability. Before going live, check:
- Permission boundaries — the agent's credentials allow only the actions it needs.
- Human approval — actions with commercial or customer impact wait for a person.
- Escalation rules — clear conditions for handing a conversation to staff.
- Data retention and processing — what the provider stores, for how long and where; whether your contracts and policies allow it.
- Deployment options — public API, a cloud provider in a suitable region, or self-hosting when data must stay on infrastructure you control.
Illustrative example: lead qualification to CRM with approval
This is an example to show the method, not a client result.
A company receives sales enquiries by WhatsApp in Arabic and English. The agent should read each message, extract the name, need and budget range if given, classify the lead, create or update the CRM record and draft a reply. Replies offering a meeting are sent automatically; anything mentioning pricing waits for a salesperson's approval.
To choose a model, the team collects 200 past enquiries, holds out 50, and tests three candidates. They compare: share of tasks completed correctly, share of correct CRM tool calls with correct fields, correct escalation of pricing questions, p95 response time and cost per completed enquiry including retries. The final choice might be a smaller model for classification with a larger model only for drafting difficult replies, but the numbers from their own held-out set decide.
Frequently Asked Questions
What is the best LLM for agentic AI?
There is no single best model. The best choice is the one that scores highest on your own representative tasks for task success, correct tool use, latency, cost and data requirements.
Which model is cheapest for agents?
The cheapest model per token is not always cheapest per completed task. Compare total cost per successful task, including retries and fallbacks, on your own workload.
Should we use a self-hosted open-weight model?
Consider it when data must stay on infrastructure you control or volumes are high and predictable. Include hosting, maintenance and evaluation effort in the comparison, and test quality on your tasks before deciding.
Next steps
If you are planning an agent, start with the workflow and the evaluation set, then the model. Learn more about our AI agents, AI automation services and business process automation.
