Enterprise AI insights, automation research & agent innovations
Build a refund-agent test matrix for approvals, duplicate requests, and uncertain payment outcomes, with a practical starting point in Ancha.
Evaluate tool-using agents with state, trace, and process checks. See how Ancha adds repeatable environments to an existing eval workflow.

A practical guide for developers and traders on using CLI-based agents to automate order placement on Kalshi prediction markets. Covers thesis-driven trading logic, real tickers, and the agentic runtime behind production-grade automation.

Signal-based bots react to noise. Thesis-driven agents understand why prices should move. Here's how causal models change prediction market trading.

Comprehensive 2025 buyer's guide to agent infrastructure: vendor landscape, architecture components, ROI, pricing models, security and procurement guidance for enterprise architects and CIOs. Includes vendor comparison matrix and downloadable POC checklist.

A practical guide to building a professional-grade terminal dashboard for monitoring Kalshi prediction markets in real time. Covers CLI tooling, agentic scanning, position tracking, and thesis-driven trade execution.

End-to-end observability for AI agents using OpenTrace and MCP. Traces, metrics, logs, and AI-specific insights combined to improve reliability, reduce MTTR, and accelerate incident response. SaaS and on-prem options, integrations, security, pricing, and real customer outcomes.
Compare static mocks, provider sandboxes, and stateful API twins. Learn when Ancha helps test agent workflows across tools, people, and time.

A systematic approach to finding mispriced prediction market contracts using causal models, orderbook analysis, and executable edge calculations.

Data-driven analysis and deployment guide: why Claude Haiku currently outperforms open-weight models for on-device agents, how to evaluate trade-offs, architecture blueprints, benchmarks, pricing, and next steps for enterprise adoption.

Comprehensive, data-driven product page comparing agent CLI tools—Claude Code, Cursor, Copilot, and OpenClaw. Includes feature matrices, benchmarks, pricing, integrations, security, case studies, and a decision guide for engineering teams evaluating CLI-based AI assistants.

How to connect your AI agent to prediction market data using SimpleFunctions MCP server — get context, inject signals, and trade on Kalshi.