Pages tagged "agents"
9 pages tagged with agents.
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- The Staged-Compiler Pattern: Chaining AI Skills from Strategy to Production An architecture pattern for AI work pipelines — two compilers joined by a frozen JSON contract, human gates at irreversible steps, parallel fan-out with central allocation.
- AI Tools Comparison: When to Use What (2026) Decision framework for choosing between ChatGPT, Claude, Gemini, and no-code agent builders based on your role and task type. May 2026 update: Gemini Omni adds any-to-any multimodal generation; Gemini 3.5 Flash surpasses prior 3.1 Pro on coding/agentic/multimodal benchmarks at 4x faster output.
- Claude Managed Agents — Anthropic's Agent Infrastructure Ready-made infrastructure for running AI agents without building your own orchestration, sandboxes, or tool execution. May 2026 update: Dreaming (between-session memory consolidation), Outcomes GA, multi-agent orchestration GA, Cowork enterprise features, 10 finance + 20 legal templates.
- Advisor Strategy — Pairing a Smarter Model as an Occasional Advisor With a Cheaper Executor Anthropic's advisor pattern (April 2026): the executor model (Sonnet or Haiku) handles tasks end-to-end while consulting an advisor model (Opus) only on hard decisions. Server-side, single API request. Sonnet+Opus advisor: +2.7pp on SWE-bench at -11.9% cost. Haiku+Opus: 41.2% on BrowseComp vs 19.7% solo, 85% cheaper than Sonnet alone.
- What's the Break-Even Point for Managed Agents vs. Self-Hosted? Exploring when Anthropic's managed infrastructure becomes more expensive than building your own
- Advisor Strategy — Smart Model Pairing for Cost-Efficiency A pattern where a cheap executor model consults an expensive advisor only when facing hard decisions, reducing costs while improving performance
- Agent Outcomes — What It Means A pattern where AI agents work toward defined completion criteria, with a separate grader evaluating success
- Claude Managed Agents vs. DIY Agent Infrastructure When to use Anthropic's managed platform vs. building your own agent orchestration with Messages API
- Multi-Agent Patterns — When Two Agents Beat One Practical patterns for combining AI agents: dispatcher + deep worker, content pipelines, and self-learning systems