Define the task, tools and boundaries
An agent should have a specific objective and a limited set of actions. Tool descriptions need clear inputs and outputs, while backend checks enforce permission regardless of model output. High-impact actions can require human review before any write occurs.
Manage state and recover from failure
Multi-step work needs persistent state, time limits and a clear response to unavailable tools. Validated structured outputs reduce ambiguous instructions, and idempotency prevents repeated execution from duplicating a change. Logs should connect a decision to its tool call and result.
Test complete tasks, not just prompts
Evaluation covers whether the task was completed correctly, whether the right tools were used and whether permissions were respected. Include incomplete requests, misleading retrieved text and provider errors. A gradual rollout can begin with recommendations before allowing selected actions.
What the scope can include
- Agent task specification and tool permissions
- State handling, action validation and review gates
- Task evaluations and trace documentation
The final deliverables, schedule, ownership and maintenance terms are agreed for your project before implementation.
A relevant engineering reference
The CEO’s portfolio describes a multi-model platform with routing, debate workflows and usage-based billing. The reference is relevant to integrating model calls with application interfaces and account usage.
Explore MetaLLMPublic CEO portfolio reference. We will discuss which experience and components are relevant to your requirements.
Questions & answers
What is the difference between an AI agent and chatbot?
A chatbot mainly responds in conversation. An agent can choose and execute tools to carry out a task. The same product may include both, but action permissions and failure recovery need their own design.
Can an agent safely update our CRM and databases?
Only within the permissions implemented by the application. Important changes can require approval, and each tool should validate inputs and authorization rather than trusting an instruction produced by the model.