Building a General-Purpose Agent: Lessons from Production
Reflections on designing and deploying Agent systems at scale. Covering architecture decisions, failure modes, and what actually works.
Why General-Purpose?
Most agent systems in production are narrowly scoped: a customer service bot, a code assistant, a data analyst. But what happens when you try to build an agent that can handle any task a user throws at it? That's the challenge I've been tackling at ByteDance.
The appeal is obvious — one system that adapts rather than dozens of specialized ones. But the engineering complexity is non-trivial. Here's what I've learned.
Architecture: The Think-Act-Observe Loop
At its core, our agent follows the ReAct paradigm: interleaving reasoning traces with actions. But production reality demands much more structure:
- Planning layer — Before execution, the agent decomposes complex tasks into sub-goals with dependency tracking.
- Tool routing — A classifier decides which tools are relevant before the LLM even sees the tool descriptions. This reduces prompt length and hallucinated tool calls.
- Memory management — Short-term (conversation context), medium-term (task state), and long-term (user preferences) each require different storage and retrieval strategies.
Failure Modes That Matter
In research papers, agents fail gracefully. In production, they fail in ways that erode user trust:
- Infinite loops — The agent retries the same failing action. We added a cycle detector with exponential backoff.
- Confident wrong answers — Worse than "I don't know." We calibrate confidence through self-consistency checks.
- Context window overflow — Long conversations lose early context. We implemented dynamic summarization at turn boundaries.
What Actually Works
1. Structured Output Over Free-form
Forcing the LLM to output structured JSON for tool calls (rather than natural language) reduced error rates by ~40%. Sounds obvious in retrospect, but the temptation to let the model be "creative" is strong.
2. Evaluation-Driven Development
We built an eval suite of 500+ scenarios before writing agent logic. Every architecture change is measured against this suite. Without this, you're flying blind.
3. Graceful Degradation
When the agent can't complete a task, it should say so clearly and hand off to a human. This single design principle improved user satisfaction scores more than any model upgrade.
Looking Forward
The gap between demo agents and production agents is still enormous. But it's closing. The key insight: agent engineering is more about systems design than prompt engineering. Treat it like building a distributed system, not writing a clever prompt.
The best agent is one that knows when not to act.