I’ve been obsessed with Agentic Workflows lately, and I just found the "missing link" for anyone struggling with agent hallucinations and massive API bills.

It’s called King Context, and it’s an open-source framework that replaces messy vector searches with structured Corpus Engineering.

The GitHub Repo:https://github.com/deandevz/king-context

Why this is a complete paradigm shift:

1. The "Corpus" Method: Instead of just "chunking" data, it synthesizes it into a specialized corpus. You can generate a corpus from any source (docs, web research, internal notes) and refine it. It’s like giving your agent a custom-built brain instead of a pile of random papers.
2. Metadata-First Retrieval: It uses a tiered approach (metadata -> preview -> full read). This stopped my agents from "hallucinating" on missing context because they can verify if the information exists before they consume the tokens.
3. Solving the Skill Bottleneck: By using "Skills" alongside a specialized Corpus, you can build multi-agent workflows where one agent acts as a researcher (building the corpus) and the other acts as an expert (executing with 100% facts).

The Numbers (Benchmarked against Context7):

• Accuracy: 38/38 correct facts (100%) vs 32/38.
• Hallucinations: ZERO (0.0) per query.
• Efficiency: 3.2x fewer tokens per request.
• Speed: Up to 170x faster metadata hits.

I’ve been talking to the dev (@deandevz), and the roadmap for Corpus Refinement (automatically pruning noisy data) is going to change how we build production-grade agents.

If you are tired of agents getting lost in large codebases or documentation, you need to check this out. It’s local-first, transparent, and built for the "Vibe Coding" era where context is everything.

Check it out here:https://github.com/deandevz/king-context

Would love to hear from anyone else trying to move away from traditional RAG. How are you handling context bloat?

One thing I’ve started noticing with MCP-based agents is that performance degrades much earlier than most people expect, especially once the number of integrations becomes large.

Small setups work surprisingly well. A few integrations, a handful of tools, manageable schemas, and the agent behaves predictably. The problems usually begin once teams start connecting the systems they actually use in production. Slack, Gmail, GitHub, Linear, Notion, databases, deployment tooling, internal APIs, monitoring systems. The integration surface grows very quickly.

At that point, the issue stops being “model intelligence” and starts becoming a context management problem.

Most MCP servers expose many tools, and each tool brings descriptions, parameter schemas, examples, and edge cases into the prompt space. Individually this feels harmless, but collectively it creates a very noisy environment for the model to reason inside. The agent spends more effort understanding the tool ecosystem than solving the task itself.

You can partially reduce the problem with lazy loading or dynamic tool visibility, but those approaches still inherit the same scaling issue underneath. The total surface area keeps growing.

I recently came across this open-source project Corsair that takes a different approach, and I thought the design was genuinely interesting.

Instead of exposing hundreds of tools directly, it exposes four generic primitives:

• setup and authentication
• operation discovery
• schema inspection
• execution

The important detail is that schemas are fetched only when the agent decides it needs them. The model first discovers available operations, then inspects a specific schema on demand, and finally executes the workflow.

That keeps the tool surface effectively constant regardless of how many integrations exist underneath.

The design feels much closer to how humans interact with unfamiliar systems. You first discover what capabilities exist, then inspect the details you need, and only then perform the action. Most current MCP ecosystems invert this by front-loading the entire integration surface into context immediately.

I suspect a lot of current agent reliability issues are really interface design problems. As integration counts grow, the systems that scale will probably be the ones that minimize what the model has to hold in working memory at any given moment.