Agents development is the work of turning a language model into a reliable worker: giving it a clear job, a small set of safe tools, the right knowledge and memory, guardrails, and a way to measure how well it performs. In production, the architecture and testing matter more than the choice of model. This guide covers the main design decisions. If you're new to the topic, start with what AI agents are.
Single agent or multi-agent?
| Single agent | Multi-agent | |
|---|---|---|
| How it works | One model with one set of instructions and tools | Several specialised agents, coordinated by an orchestrator or a fixed workflow |
| Best for | One focused job, such as support or lead qualification | Long tasks with distinct stages, such as research, then drafting, then review |
| Trade-offs | Simpler, cheaper, easier to debug | More flexible, but more tokens, latency and failure points |
Start with a single agent. Split into several agents only when one agent's instructions become too long to follow reliably, or when stages need different tools or permissions.
Workflow or autonomous agent?
Many successful "agents" are really workflows: fixed steps where a model handles the parts that need judgement, such as classifying a request or drafting a reply. Workflows are predictable and easy to test. Use a fully autonomous loop, where the model chooses its next step, only when the path genuinely varies from case to case. Mixing both is common: a workflow that calls an agent for one flexible step.
The core components of a production agent
Tools and integrations
- Give each tool one clear purpose, a descriptive name and typed inputs.
- Enforce permissions in your API, so the agent can never do more than the user it acts for.
- Use the Model Context Protocol (MCP) to expose tools and data in a standard way that works across models and assistants.
Knowledge
Ground the agent in your documents and data with retrieval-augmented generation (RAG), and give it live data through read-only tools.
Memory
- Short-term: the current conversation and the results of earlier steps.
- Long-term: facts worth keeping between sessions, such as a customer's preferences, stored deliberately rather than by saving everything.
Guardrails
Limits on steps and spend, approval for high-impact actions, checks for personal data and prompt injection, and a clear handover to people.
Agent frameworks compared
| Option | Good for |
|---|---|
| Provider agent SDKs (for example the Claude Agent SDK or OpenAI Agents SDK) | Fast start with one model provider, built-in tool use and tracing |
| LangGraph | Stateful, graph-based workflows with branching, retries and human approval steps |
| LlamaIndex | Agents that lean heavily on document retrieval |
| CrewAI | Role-based multi-agent prototypes |
| Custom loop | Full control and minimal dependencies for focused agents |
Frameworks change quickly. Keep your business logic, tools and prompts separate from the framework so you can switch later.
Evaluation and observability
- Build a test set of real cases with the correct outcome, including tricky and adversarial ones.
- Score separately: was the right information retrieved, the right tool called with the right inputs, and the final answer correct?
- Trace every run in production: prompts, tool calls, latency and cost.
- Review failures weekly and turn them into new test cases.
Deployment checklist
- Timeouts, retries and fallbacks when a model or tool is unavailable.
- Rate limits and cost alerts.
- Versioned prompts and tools, with the test set run before each release.
- Data-retention and privacy settings agreed with your model provider.
Key takeaways
- Prefer a single agent or a workflow first; add agents and autonomy only when needed.
- Narrow, permissioned tools, grounded knowledge and guardrails make agents reliable.
- Evaluation and tracing are part of the product, not an afterthought.
Planning an agent? Follow our step-by-step build guide or talk to AppifyDevs about agents development.