Accurately forecasting large stock price movements after corporate earnings announcements is a longstanding challenge. Existing methods–sentiment lexicons, fine-tuned encoders, and standalone LLMs–often **lack temporal-causal reasoning** and are prone to **narrative bias**, echoing overly optimistic managerial tone. We introduce **Context-Enriched Agentic RAG (CARAG)**, a retrieval-augmented framework that deploys a team of cooperative LLM agents, each specializing in a distinct analytical task: evaluating historical performance, assessing the credibility of guidance, or benchmarking against peers.Agents retrieve structured evidence from a Causal-Temporal Knowledge Graph (CTKG) built from financial statements and earnings calls, enabling grounded, context-rich reasoning. This design mitigates LLM hallucinations and produces more objective predictions.Without task-specific training, our system achieves state-of-the-art zero-shot performance across NASDAQ, NYSE, and MAEC datasets, outperforming both larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe, beating market benchmarks (S P 500 and Nasdaq) for the same forecasting horizon. Code, datasets, prompts, and implementation details are included in the supplementary material to ensure full reproducibility.