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### The Power of Workflows |
Graph based agents check every step. Important when mistakes cost money. VoltAgent's workflow system lets you: |
- Conditional execution with `.andWhen()` |
- Parallel processing with `.andAll()` |
- Race conditions with `.andRace()` |
LLMs decide what happens next at each step. As models get smarter, we might go back to simpler designs. Pick a framework that works now and later. |
## Common Mistakes When Building Agents |
Everyone makes these mistakes. Here's how to avoid them. |
#### Over Engineering from Day One |
Start simple. Don't build a complex multi agent system when a single agent will do. Most teams waste months on fancy architectures they don't need. Get something working first, then add complexity. |
#### Ignoring Error Handling |
Agents fail in weird ways. LLMs timeout. APIs go down. Context windows overflow. Build retry logic, fallbacks, and graceful degradation from the start. Log everything. You'll need it when debugging at 3 AM. |
#### Poor Prompt Engineering |
Bad prompts kill agent performance. Too vague and agents hallucinate. Too specific and they can't adapt. Test prompts with edge cases. Version control them. A/B test in production. Small prompt changes can 10x your results. |
#### No Human in the Loop |
Pure automation sounds great until agents start doing damage. Build approval workflows for critical actions. Add confidence scores. Let humans override decisions. Systems should augment humans, not replace them. |
#### Skipping Evaluation |
How do you know if your agent works? Most teams launch without proper testing. Build eval datasets. Track success metrics. Run regression tests when you change prompts. Without evals, you're flying blind. |
#### Context Window Management |
Agents need memory but context windows are limited. Stuffing everything into context makes agents slow and expensive. Build smart retrieval. Summarize old conversations. Delete irrelevant info. Good context management cuts costs by 90%. |
#### Security as an Afterthought |
Agents can leak data, execute harmful code, or get prompt injected. Sanitize inputs. Limit tool access. Run in sandboxes. One compromised agent can wreck your whole system. |
#### Wrong Model for the Job |
GPT-4 for everything is expensive and slow. Use smaller models for simple tasks. Fine tune for specific domains. Mix models in pipelines. The right model strategy saves money and improves speed. |
## Production and Observability |
Real world agents can't just "work". They need to work all the time. API + queue setups handle tons of requests without dropping any. |
**VoltOps** is built just for watching AI agents. See traces, metrics, and logs in real time. Works with OpenTelemetry, so you can track everything your agents do. |
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Track costs as they happen. See which prompts ran, token counts, and how long things took. All in one dashboard. |
Testing agents is huge. VoltAgent uses **Viteval** to check if agents work right. Test for accuracy, catch hallucinations, check if answers make sense. All automatic. |
## Ecosystem and Integrations |
You need lots of tools to build agents. **VoltAgent** works with everything: |
### LLM Provider Support |
Works with all the big LLM providers: |
- **OpenAI** (GPT-4, GPT-3.5) |
- **Anthropic** (Claude 3 family) |
- **Google AI** (Gemini models) |
- **Groq** (for fast inference) |
- **[Vercel AI SDK](https://voltagent.dev/docs/providers/vercel-ai)** with 30+ provider support |
Switch providers with one line of code. No lock in. [View all providers →](https://voltagent.dev/docs/getting-started/providers-models) |
### Vector Database Integrations |
For [RAG (Retrieval Augmented Generation)](https://voltagent.dev/docs/rag/overview) applications: |
- **[Pinecone](https://voltagent.dev/docs/rag/pinecone)**: Cloud based, high performance |
- **[Chroma](https://voltagent.dev/docs/rag/chroma)**: Open source, self hosted option |
- **[Qdrant](https://voltagent.dev/docs/rag/qdrant)**: Rust based, fast and reliable |
### Modern Framework Support |
- **[Next.js](https://voltagent.dev/docs/integrations/nextjs)** integration for full stack AI applications |
- **[MCP (Model Context Protocol)](https://voltagent.dev/docs/agents/mcp)** support for extensible tools |
### Memory and Storage Options |
For different deployment scenarios: |
- **[LibSQL](https://voltagent.dev/docs/agents/memory/libsql)**: Default for local development |
- **[PostgreSQL](https://voltagent.dev/docs/agents/memory/postgres)**: Production ready, scalable |
- **[Supabase](https://voltagent.dev/docs/agents/memory/supabase)**: Serverless with real time features |
- **[InMemory](https://voltagent.dev/docs/agents/memory/in-memory)**: For test environments |
Build fast prototypes or production systems. VoltAgent handles both. |
## Industry Dynamics |
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