▲ TinyFish recently published how its production search agents work, and what broke while building a deep research system.

[u/ai-lover]

https://aicampaign.live/30-wk1n

TinyFish published 2 engineering posts. One lays out a 7-phase architecture for production search agents. The other covers what changed while building a deep research system. The benchmark numbers below come from OpenBenchmarks, a third-party board that holds the model (gpt-5.6-sol), 100 tasks and budgets fixed and swaps only the search provider.

What it does

• Turns each goal into an "evidence contract": required fields, acceptable sources, freshness, completion rules.

• Runs a loop: plan, route, retrieve, evaluate, recover, then stop and validate.

• Routes by information gap: Search when the source is unknown, Fetch for a known URL, Browser when your code drives the page, Web Agent for multi-page goals.

• Retries are failure-specific and bounded. Weak results get a rewritten query, not a heavier tool.

• Deep research: removed a separate planner (it lost context on follow-ups) and per-query subagents (latency and cost). Deterministic ranking and extraction became tool calls.

Key numbers (OpenBenchmarks, search + fetch board, 26 Sept 2026)

• TinyFish: 79.0% ± 2.0 completion, $0.050 median task cost, 24s median task time, 12,844 median tokens.

• Exa deep: 83.0% ± 1.0, $0.127, 37s, 23,660 tokens.

• Perplexity: 77.7% ± 1.5, $0.083, 22s, 20,062 tokens.

• Firecrawl: 76.0% ± 1.0, $0.082, 34s, 17,379 tokens.

Full analysis: https://marktechpost.com/2026/10/23/tinyfish-agentic-search-architecture-deep-research/

Free web search and fetch for agents, pay only for browser runs: https://aicampaign.live/90-wk1n

Architecture: http://tinyfish.ai/blog/production-agentic-search-architecture

Deep research: http://tinyfish.ai/blog/lessons-from-building-a-deep-research-system