Tag: Agent

DeepSeek Harness's model interface layer unifies Message, ContentBlock, GenerateOptions, and StreamChunk abstractions, turning heterogeneous model integrations—DeepSeek, OpenAI, LM Studio—into a pluggable translator system. This beginner-friendly post explains how the interface layer eliminates multi-model integration drudgery and how eight adapter rules let upper-layer business logic swap models without changing a single line of code.

In the past year, Agent products have proliferated, but DeepSeek Harness (DSH) asks a more fundamental question: if you want to build your own agent rather than use someone else's, what do you actually need? This post unpacks why DSH is not yet another terminal product, but a composable, auditable, and extensible agent infrastructure for builders.

A deep-dive analysis of the independent third-party Agent memory infrastructure industry in H1 2026, comparing core players including Mem0, Clipto, Letta, Zep, Cognee, Hindsight, and Microsoft Memora across technical routes, benchmarks, and commercial endgame scenarios.

After upgrading from vector retrieval to GraphRAG, my Agent developed four pathologies: entity duplication, relation inflation, zombie memories, and query timeouts. This article diagnoses memory health with four quantitative metrics and proposes four fixes: deduplication, structured pruning, tiered storage, and graph optimization.

From legal minefields to technical self-healing—a practical defense handbook for AI architects. This article provides a 'Pre-Check → In-Flight → Post-Check' three-stage closed-loop framework for managing the full lifecycle of Agent external information sources.

From LangGraph to Actor models, from 'stop and ask humans' to 'keep running in async messages'—the evolution path of industrial-grade Agents is more complex than it looks.

Agent memory loss is not accidental—it's architecture-determined inevitability. This article dismantles the 'number games' big tech plays with long context and memory, and reveals what application-layer architectures are actually worth investing in.

While the Agent space appears booming, 80% of developers are actually just writing 'glue code' (L1 orchestration layer). Real technical barriers lie in L2 engineering and L3 model layers, and 2027-2028 will be the critical inflection point for industry consolidation.

Data shows 90% of Agent projects fail in production environments. The problem is not storage capacity, but that memory never truly 'comes alive.' This article dissects five structural defects.

Data shows COT has low investment with obvious accuracy improvement; ReAct is the balanced choice; Agent theoretically has the highest accuracy but costs increase sharply. Accuracy is a system capability designed into existence.

Exploring the core paradigm shift in the AI-Native era, evolving from traditional tool-centric thinking to agent-first architecture design.