We built AgentForge to solve our own problem. Here's what 6 months of production multi-agent deployment taught us.
Lesson 1: Start with Failure Modes, Not Success Cases
Everyone designs for the happy path. But in multi-agent systems, the failure modes multiply:
- Agent A succeeds but takes 30s → Agent B times out waiting
- Agent A returns malformed JSON → Agent B crashes parsing
- Two agents try to write the same file → Race condition
Design your orchestration around "what breaks" first.
Lesson 2: Observability Is Not Optional
You need per-agent execution traces. Not just logs — structured traces showing:
- Input parameters (exact values, not summaries)
- Output before any post-processing
- Retry attempts with backoffs
- Circuit breaker state transitions
We built this into AgentForge's execution engine. Every run generates a JSON trace you can replay for debugging.
Lesson 3: Agents Need Memory, But Not Infinite Memory
Unbounded conversation history degrades performance. We use a sliding window + summary strategy:
- Keep last N turns verbatim
- Summarize older turns into structured context
- Let agents explicitly "remember" key facts via a memory store
Lesson 4: Cost Optimization Is Architecture
Running 5 agents × 4K tokens × GPT-4 gets expensive fast. Our approach:
- Router agent determines which specialist to invoke (cheaper model)
- Specialist agents use larger models only when needed
- Response caching for deterministic queries
Result: 60% cost reduction vs. naive implementation.
The Stack
- Python 3.11+
- Pydantic for schema validation
- AsyncIO for concurrent agent execution
- SQLite/Redis for state persistence
- WebSocket for real-time monitoring UI
Open source. No VC pitch. Just code that works.
https://github.com/agentforge-cyber/agentforge-mvp
Join us: https://discord.gg/Qy6HKHsqP
Posted on 2026-07-21 by the AgentForge team.
Top comments (1)
Starting from failure modes is the right instinct. Multi-agent systems get weird long before they get smart: malformed handoffs, duplicate writes, retries that look like progress, and "success" states that hide partial failure.
The observability section resonated too. Once you need per-agent inputs, outputs, retries, and cost on the same timeline, plain logs stop being enough. That is a big part of why we built agent-inspect around local-first execution traces and tool-call receipts for TypeScript agent workflows.
Curious whether AgentForge keeps memory and trace data in the same model, or if you separate those concerns operationally?