restarts
Beyond Market Intelligence keeps restarts in one place: 3 stories so far. The section currently leads with “Build Persistent Memory for Your AI Agent with a Single JSON File”, “Postgres: Your Durable Orchestrator for Workflows Without External Tools”, and “Build AI workflows that survive production without slowing your experiments”. A single JSON file can now give your Python AI agent memory that survives a restart. Most teams assume durable workflows demand an external orchestrator, but Postgres can handle that heavy lifting on its own. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every restarts story on Beyond Market Intelligence, newest first.
Build Persistent Memory for Your AI Agent with a Single JSON File
A single JSON file can now give your Python AI agent memory that survives a restart. Three Python classes, an OpenRouter key, and gpt-4.1-mini equip the agent with a calculator, a stub web search, and full conversation recall. This is a practical step toward agents that actually remember context between sessions. If you're building autonomous tools, this approach removes a major friction point. For deeper context on keeping agents both capable and secure, see our coverage of Edera's Object Capability Bounded Authority model.
Postgres: Your Durable Orchestrator for Workflows Without External Tools
Most teams assume durable workflows demand an external orchestrator, but Postgres can handle that heavy lifting on its own. Raman Varma shows how the database itself becomes the state store and coordination layer, using `SKIP LOCKED` for concurrent processing, primary-key checkpoints for idempotency, and leases to survive crashes. Even workflow sleeps and human approvals persist as database state. It's a pragmatic, accessible approach for teams already comfortable with Postgres.
Build AI workflows that survive production without slowing your experiments
Every AI workflow faces a quiet tension. Production durability demands persisting every step, so a crash or deploy doesn't erase progress. But that same safety net weighs down the fast, throwaway loop you need to judge an LLM's output. Mateus Moury's pattern tackles this head-on, separating the two concerns instead of forcing a compromise. It's a practical framework for teams tired of choosing between resilience and speed. For more on AI's practical edges, our guide to distributed training offers a useful parallel.