An agentic AI pipeline that classifies political stance using tool-augmented multi-agent architectures with collaborative reasoning loops — going beyond single-model inference.
Political stance detection — classifying whether a text leans for, against, or is neutral toward a given topic — is a fundamentally ambiguous task. Single-model classifiers struggle with nuance, irony, and domain-specific context that requires cross-referencing multiple knowledge sources.
This project builds a multi-agent framework where specialised agents collaborate to reach a stance classification. One agent retrieves contextual evidence, another performs logical reasoning, and a final arbiter synthesises the conclusion — mimicking how a human analyst might cross-check sources before forming a verdict.
The system is built around three specialised agents operating in an orchestrated pipeline:
Each agent is backed by a fine-tuned language model with access to specific tools — the retriever uses a vector store and search API, the reasoner implements structured chain-of-thought prompting, and the arbiter applies a learned confidence calibration layer.
Agent coordination overhead: Multi-agent pipelines introduce latency. Optimised by running retrieval in parallel with initial reasoning and caching frequent context patterns.
Conflicting agent outputs: Agents sometimes reached contradictory stances. Resolved using an explicit disagreement protocol where the arbiter requests additional evidence before committing to a label.
Domain shift: Political language varies drastically by region, platform, and time period. Applied domain-adaptive fine-tuning with data augmentation to improve cross-domain generalisation.
The multi-agent approach outperformed a single fine-tuned transformer baseline by 12 percentage points on F1, with the most significant gains on ambiguous and ironic texts where contextual reasoning is critical.