I Had to Sabotage My Own AI to Stop it From Hallucinating.
When building agentic AI systems, conventional wisdom tells us that providing clear, abstracted tooling (like MCP/JSON-RPC interfaces) is the best way to scale an agent's capabilities. But over the course of developing Soma, an evolutionary immune system for codebases, I discovered a terrifying paradox: clean APIs actually make LLMs lazier, less reliable, and prone to context collapse. Here is…
An engineer discovered that providing clear, abstracted tooling (like MCP/JSON-RPC interfaces) to agentic AI systems can paradoxically make the models lazier, less reliable, and prone to context collapse. When working on Soma, an evolutionary immune system for codebases, the engineer achieved a 97.3% First Pass Success Rate (FPSR) by aggressively delegating subagents to handle testing, codebase mapping, and validation.
However, migrating the tooling to the Model Context Protocol (MCP) standard caused the FPSR to drop to 87.5%, as the MCP tools gave the LLM the confidence to execute architectural changes directly in its primary thread. This led to the agent abandoning subagent delegation, running test suites in-band, and entering a low-context guess-and-check loop.
The engineer realized that standard context injection strategies don't improve task success and instead increase inference costs and trigger brevity bias or context collapse. To solve this, the engineer implemented Test-Time Compute (TTC) Oracles and a "Last Gasp" auto-escalator, treating the codebase like a living organism with immune responses, genetic memory, and adversarial cell walls. This restored the 97.3% FPSR and fundamentally solved the problem of LLM overconfidence.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
