Collective estimation is a variant of collective decision-making where agents reach consensus on a continuous quantity through social interactions. Achieving precise consensus is complex due to the co-evolution of opinions and the interaction network. While homophilic networks may facilitate estimation in well-connected systems, disproportionate interactions with like-minded neighbors lead to the emergence of echo chambers and prevent consensus. Our agent-based simulations confirm that, besides limited exposure to attitude-challenging opinions, seeking reaffirming information entrap agents in echo chambers. To overcome this, agents can adopt a stubborn state (Messengers) that carry data and connect clusters by physically transporting their opinion. We propose a generic approach based on a Dichotomous Markov Process, which governs probabilistic switching between behavioral states and generates diverse collective behaviors. We study a continuum between task specialization (no switching), to generalization (slow or rapid switching). Messengers help the collective escape local minima, break echo chambers, and promote consensus.