Developers · Flagship guide
How to Build an AI Agent (Step by Step)
Build an AI agent by choosing a model, defining tools, implementing a planning loop and adding a memory layer — the step-by-step path from prototype to production agent.
Build path
Step 1
Define the agent’s job
Scope, tools needed and success metrics before writing code.
- What problem does the agent solve? (support, coding, personal assistant)
- What tools does it need? (APIs, search, code execution, memory)
- How do you measure success? (LOCOMO recall@k, task completion, CSAT)
- Does it need cross-session memory? (almost always yes for production)
Step 2
Choose model and orchestration
LLM selection plus agent loop framework — LangGraph, Letta or custom.
- LangGraph — graph-based agent loops + LangMem for LTM
- Letta — MemGPT paging for long-context agents
- Custom loop — retrieve → generate → write each turn
- n8n / low-code — HTTP nodes to Engram/Mem0 APIs
Step 3
Add tools
APIs, code execution and memory tools — agents act on the world, not just chat.
Include memory as a first-class tool: search_memory, write_memory, forget_memory. The agent decides when to persist and retrieve facts.
Step 4
Add memory
The critical step — without memory, your agent forgets everything between sessions.
- Pick a framework: Engram (Weaviate), Mem0 (fastest POC), Zep (temporal graph), Letta (paging)
- Scope by
user_idon every write and retrieve - Retrieve top-k memories before each LLM call
- Extract and write facts after each response
- Validate on LOCOMO before production
Step 5
Evaluate and deploy
LOCOMO/LongMemEval in CI; observability; production hardening.
- Run LOCOMO subset on each deploy — gate on recall@k regression
- Log memory writes/retrievals with trace IDs
- Set retrieval latency SLAs (sub-200 ms p99 for support bots)
- Implement delete API for privacy compliance
FAQ
Frequently asked questions
Fastest agent stack in 2026?
Engram for Weaviate-native apps; Mem0 managed API for framework-agnostic POC; LangGraph + LangMem for LangChain shops. Pick based on existing infra.
Is memory required on day one?
For multi-session agents, yes. Single-shot prototypes can skip LTM but production assistants need memory from the start.
LangGraph vs Letta for building agents?
LangGraph = flexible graph loops + LangMem LTM. Letta = MemGPT paging for long-context agents. DMR 93.4% (Packer et al., 2023).
How do you add Mem0 to an agent?
Install SDK → retrieve before LLM → write after response → scope by user_id. LOCOMO J 66.9 (Chhikara et al., 2025). See add memory guide.
Building agents with n8n?
HTTP nodes to Engram/Mem0/Zep APIs — retrieve before LLM node, write after response. Same memory lifecycle as code frameworks.
Production deployment checklist for agents?
user_id scoping, LOCOMO in CI, retrieval logging, delete API, cost caps. See memory management.
Building agents with Engram?
Weaviate Cloud + Engram API — send conversation, async extraction, retrieve via hybrid search. GA June 2026. See Engram explained.
Path for coding agents specifically?
Repo RAG + per-dev memory (Engram/Mem0) + Letta for long refactor threads. See coding agents use case.